Methods, arrays and uses thereof

ABSTRACT

The present invention provides a method for diagnosing or determining a pancreatic cancer-associated disease state comprising or consisting of the steps of: (a) providing a sample from an individual to be tested; and (b) determining a biomarker signature of the test sample by measuring the presence and/or amount in the test sample of two or more biomarkers selected from the group defined in Table A; wherein the presence and/or amount in the test sample of two or more biomarkers selected from the group defined in Table A is indicative of the pancreatic cancer-associated disease state in the individual; uses and methods of determining a pancreatic cancer-associated disease state, and methods of treating pancreatic cancer, together with arrays and kits for use in the same.

FIELD OF INVENTION

The present invention provides in vitro methods for determining a pancreatic cancer-associated disease state (such as pancreatic cancer presence or risk of pancreatic cancer), as well as arrays and kits for use in such methods.

BACKGROUND

Pancreatic cancer is a relatively rare but highly lethal cancer with a 5-year survival rate of less than 10% (Ilic and Ilic, 2016). Its high mortality makes it the third leading cause of cancer-related death in the United States (Rawla et al., 2019). One factor behind this dismal record is the lack of early and disease specific clinical symptoms. At the time of diagnosis, patients have often developed late-stage disease and only approximately 15-20% of the patients have resectable tumors (Conlon et al., 1996; Sohn et al., 2000). Significantly better outcomes have been reported for smaller tumors detected at an earlier stage. A 5-year survival rate of 30% to 60% in tumors less than 20 mm in size and even exceeding 75% in tumors less than 10 mm have been reported (Shimizu et al., 2005, Kenner et al., 2016).

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer accounting for more than 90% of all pancreatic malignancies (McGuigan et al., 2018). Rapid tumor progression, early metastasis, and resistance to conventional chemotherapies are hallmarks of PDAC (Orth et al., 2019). Since complete surgical removal is the only potentially curative treatment for PDAC, biomarkers for early detection are urgently needed. The most evaluated biomarker for PDAC, CA19-9, suffers from inadequate specificity and sensitivity with elevated levels in several benign diseases (e.g. chronic pancreatitis and obstructive jaundice), as well as a complete absence in patients that are Lewis blood type negative (about 5-10% of the population). Consequently, the use of serum CA19-9 by itself is not recommended for screening, but can provide important information concerning prognosis, response to chemotherapy as well as predict post-operative recurrence (Ballehaninna and Chamberlain, 2011).

Taken together, there remains a need for improved methods of diagnosing pancreatic cancers such as PDAC, particularly in the early stages of the disease and it has been postulated that earlier diagnosis would result in increased survival for patients with PDAC and that selected high-risk groups could benefit greatly from a non-invasive test for detecting cancer development.

SUMMARY OF THE INVENTION

Accordingly, a first aspect of the invention provides a method for diagnosing or determining a pancreatic cancer-associated disease state comprising or consisting of the steps of:

-   -   (a) providing a sample from an individual to be tested; and     -   (b) determining a biomarker signature of the test sample by         measuring the presence and/or amount in the test sample of one         or more biomarkers selected from the group defined in Table A;         wherein the presence and/or amount in the test sample of the one         or more biomarkers selected from the group defined in Table A is         indicative of the pancreatic cancer-associated disease state in         the individual.

In a preferred embodiment, step (b) comprises determining a biomarker signature of the test sample measuring the presence and/or amount in the test sample of two or more biomarkers selected from the group defined in Table A, wherein the presence and/or amount in the test sample of the two or more biomarkers selected from the group defined in Table A is indicative of the pancreatic cancer-associated disease state in the individual.

In an additional or alternative embodiment, step (b) comprises determining a biomarker signature of the test sample measuring the presence and/or amount in the test sample of two or more biomarkers selected from the group defined in Table A(i)-(vi), wherein the presence and/or amount in the test sample of the two or more biomarkers selected from the group defined in Table A(i)-(vi) is indicative of the pancreatic cancer-associated disease state in the individual.

TABLE A Biomarkers (and associated Uniprot IDs where available) Part (i) OPG (O00300)/VWF (P04275) Part (ii) GSN (P06396)/HADH2 (Q99714) Part (iii) IGFBP3 (P17936) Part (iv) Complement Factor B (P00751) Part (v) MUC16 (CA125) (Q8WXI7)/FCN2 (Q15485)/MASP2 (O00187) Part (vi) Complement C4 (P0C0L4/5) Complement C5 (P01031) Cystatin C (P01034) Part (vii) Carbohydrate Antigen 19-9 (CA19-9)

Thus, in one embodiment, the method comprises determining a biomarker signature of the test sample, which enables a diagnosis to be reached in respect of the individual from which the sample is obtained.

By “pancreatic cancer associated disease state” we include pancreatic cancer presence per se and the risk of having or of developing pancreatic cancer. In particular, we include the presence of pancreatic ductal adenocarcinoma (PDAC) at various stages.

In specific embodiments, the methods of the invention permit:

-   -   (i) diagnosis of early pancreatic cancer; and/or     -   (ii) diagnosis of pancreatic cancer (i.e. early or late).

By “biomarker” we include any naturally occurring biological molecule, or component or fragment thereof, the measurement of which can provide information useful in the diagnosis of pancreatic cancer. Thus, in the context of Table A, the biomarker may be the protein, or a polypeptide fragment or carbohydrate moiety thereof. Alternatively, the biomarker may be a nucleic acid molecule, such as a mRNA, cDNA or circulating tumour DNA molecule, which encodes the protein or part thereof.

By “diagnosis” we include determining the presence or absence of a disease state in an individual (e.g., determining whether an individual is or is not suffering from early stage pancreatic cancer or late stage pancreatic cancer).

By “early pancreatic cancer” we include or mean pancreatic cancer comprising or consisting of stage I and/or stage II pancreatic cancer.

The methods of the invention are suitable for testing a sample from any individual who is suspected of having, or at risk of developing, a pancreatic cancer-associated disease state. For example, the individual may be from one of the following groups with an elevated risk of having or developing pancreatic cancer:

-   -   (i) Individuals with a family history of pancreatic cancer or         certain hereditary predispositions (e.g. Peutz-Jeghers         syndrome);     -   (ii) Individuals diagnosed with diabetes, e.g. new-onset         diabetes (e.g. type II), especially those aged 50 years or over;         and/or     -   (iii) Individuals with symptoms suggestive or consistent with         pancreatic cancer, e.g. pain in the upper abdomen or upper back,         loss of appetite, weight loss, jaundice (yellow skin and eyes,         and dark urine), indigestion, nausea, vomiting and/or extreme         tiredness (fatigue)).

The individual may also be an individual with a benign pancreatic or biliary disease, e.g. acute and chronic pancreatitis, diabetes, liver disease, pancreatic cyst, gallstone disease and IgG4 disease.

In an additional or alternative embodiment the method is suitable for distinguishing an individual with a pancreatic cancer-associated disease state from an individual without pancreatic cancer but with symptoms suggestive or consistent with pancreatic cancer. For example, the individual without pancreatic cancer from which an individual with a pancreatic cancer-associated disease state may be distinguished may have a benign pancreatic or biliary disease, e.g. acute and chronic pancreatitis, diabetes, liver disease, pancreatic cyst, gallstone disease and IgG4 disease.

Thus, in one embodiment, the methods of the invention provide a qualitative result for the detection of pancreatic abnormalities in individuals with increased risk of developing PDAC.

In a specific embodiment, the methods of the invention permit:

-   -   (a) the diagnosis of early pancreatic cancer; and     -   (b) the diagnosis of late pancreatic cancer.

Advantageously, the methods of the invention also enable the differentiation between pancreatic cancer and chronic pancreatitis in an individual.

By “early pancreatic cancer” (or “early stage pancreatic cancer”) we include or mean pancreatic cancer comprising or consisting of stage I and/or stage II pancreatic cancer, for example as determined by the American Joint Committee on Cancer (AJCC) TNM system (e.g., see:

http://www.cancer.ora/cancer/pancreaticcancer/detailedauide/pancreatic-cancer-staging and AJCC Cancer Staging Manual (7^(th) ed.), 2011, Edge et al., Springer which are incorporated by reference herein).

The TNM cancer staging system is based on 3 key pieces of information:

-   -   T describes the size of the main (primary) tumour and whether it         has grown outside the pancreas and into nearby organs.     -   N describes the spread to nearby (regional) lymph nodes.     -   M indicates whether the cancer has metastasized (spread) to         other organs of the body. (The most common sites of pancreatic         cancer spread are the liver, lungs, and the peritoneum—the space         around the digestive organs.)

Numbers or letters appear after T, N, and M to provide more details about each of these factors.

T Categories

-   -   TX: The main tumour cannot be assessed.     -   T0: No evidence of a primary tumour.     -   Tis: Carcinoma in situ (the tumour is confined to the top layers         of pancreatic duct cells). (Very few pancreatic tumours are         found at this stage.)     -   T1: The cancer is still within the pancreas and is 2         centimetres (cm) (about ¾ inch) or less across.     -   T2: The cancer is still within the pancreas but is larger than 2         cm across.     -   T3: The cancer has grown outside the pancreas into nearby         surrounding tissues but not into major blood vessels or nerves.     -   T4: The cancer has grown beyond the pancreas into nearby large         blood vessels or nerves.

N Categories

-   -   NX: Nearby (regional) lymph nodes cannot be assessed.     -   N0: The cancer has not spread to nearby lymph nodes.     -   N1: The cancer has spread to nearby lymph nodes.

M Categories

-   -   M0: The cancer has not spread to distant lymph nodes (other than         those near the pancreas) or to distant organs such as the liver,         lungs, brain, etc.     -   M1: The cancer has spread to distant lymph nodes or to distant         organs.

Once the T, N, and M categories have been determined, this information is combined to assign an overall stage of 0, I, II, III, or IV (sometimes followed by a letter). This process is called stage grouping.

Stage 0 (Tis, N0, M0): The tumour is confined to the top layers of pancreatic duct cells and has not invaded deeper tissues. It has not spread outside of the pancreas. These tumours are sometimes referred to as pancreatic carcinoma in situ.

Stage IA (T1, N0, M0): The tumour is confined to the pancreas and is 2 cm across or smaller (T1). It has not spread to nearby lymph nodes (N0) or distant sites (M0).

Stage IB (T2, N0, M0): The tumour is confined to the pancreas and is larger than 2 cm across (T2). It has not spread to nearby lymph nodes (N0) or distant sites (M0).

Stage IIA (T3, N0, M0): The tumour is growing outside the pancreas but not into major blood vessels or nerves (T3). It has not spread to nearby lymph nodes (N0) or distant sites (M0).

Stage IIB (T1-3, N1, M0): The tumour is either confined to the pancreas or growing outside the pancreas but not into major blood vessels or nerves (T1-T3). It has spread to nearby lymph nodes (N1) but not to distant sites (M0).

Stage III (T4, Any N, M0): The tumour is growing outside the pancreas into nearby major blood vessels or nerves (T4). It may or may not have spread to nearby lymph nodes (Any N). It has not spread to distant sites (M0).

Stage IV (Any T, Any N, M1): The cancer has spread to distant sites (M1).

Alternatively or additionally, by “early pancreatic cancer” (or “early stage pancreatic cancer”) we include or mean asymptomatic pancreatic cancer. Common presenting symptoms of pancreatic cancers include jaundice, abdominal pain, weight loss, steatorrhoea, and new-onset diabetes. For example, the pancreatic cancer may be present at least 1 week before symptoms (e.g., common symptoms) are observed or observable, for example, ≥2 weeks, ≥3 weeks, ≥4 weeks, ≥5 weeks, ≥6 weeks, ≥7 weeks, ≥8 weeks, ≥3 months, ≥4 months, ≥5 months, ≥6 months, ≥7 months, ≥8 months, ≥9 months, ≥10 months, ≥11 months, ≥12 months, ≥18 months, ≥2 years, ≥3 years, ≥4 years, or ≥5 years, before symptoms are observed or observable.

Thus, by “early pancreatic cancer” (or “early stage pancreatic cancer”) we include pancreatic cancers that are of insufficient size and/or developmental stage to be diagnosed by conventional clinical methods. For example, by “early pancreatic cancer” or “early stage pancreatic cancer” we include or mean pancreatic cancers present at least 1 week before the pancreatic cancer is diagnosed or diagnosable by conventional clinical methods, for example, ≥2 weeks, ≥3 weeks, ≥4 weeks, ≥5 weeks, ≥6 weeks, ≥7 weeks, ≥8 weeks, ≥3 months, ≥4 months, ≥5 months, ≥6 months, ≥7 months, ≥8 months, ≥9 months, ≥10 months, ≥11 months, ≥12 months, ≥18 months, ≥2 years, ≥3 years, years, or years, before the pancreatic cancer is diagnosed or diagnosable by convention clinical methods.

The contemporary best practice for clinical pancreatic cancer diagnosis will be well known to the person of skill in the art, however, for a detailed review see Ducreux et al., 2015, ‘Cancer of the pancreas: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up’ Annals of Oncology, 26 (Supplement 5): v56-v68 which is incorporated by reference herein.

Conventional clinical diagnoses (e.g., “diagnosed by conventional clinical methods”) include CT scan, ultrasound, endoscopic ultrasound, biopsy (histopathology) and/or physical examination (e.g., of the abdomen and, possibly, local lymph nodes). In one embodiment by “conventional clinical diagnoses” (and the like) we include the pancreatic cancer diagnosis procedures set out in Ducreux et al., 2015, supra.

Conventional clinical diagnoses (and the like) may include or exclude the use of molecular biomarkers present in bodily fluids (such as blood, serum, interstitial fluid, lymph, urine, mucus, saliva, sputum, sweat) and or tissues.

It will be appreciated by persons skilled in the art that the early pancreatic cancer may be a resectable pancreatic cancer.

By “resectable pancreatic cancer” we include or mean that the pancreatic cancer comprises or consists of tumours that are (and/or are considered) capable of being removed by surgery (i.e., are resectable). For example, the pancreatic cancer may be limited to the pancreas (i.e., it does not extend beyond the pancreas and/or have not metastasised).

In one embodiment, the early pancreatic cancer comprises tumours of 30 mm or less in all dimensions (i.e., in this embodiment individuals with early pancreatic cancer do not comprise pancreatic cancer tumours of greater than 30 mm in any dimension), for example, equal to or less than 29 mm, 28 mm, 27 mm, 26 mm, 25 mm, 24 mm, 22 mm, 21 mm, 20 mm, 19 mm, 18 mm, 17 mm, 16 mm, 15 mm, 14 mm, 13 mm, 12 mm, 11 mm, 10 mm, 9 mm, 8 mm, 7 mm, 6 mm, 5 mm, 4 mm, 3 mm, 2 mm, 1 mm or equal to or 0.1 mm in all dimensions. Alternatively or additionally, the pancreatic cancer tumours of 30 mm or less in all dimensions are at least 2 mm in one dimension. Alternatively or additionally, the pancreatic cancer tumours of 30 mm or less in all dimensions are at least 2 mm all dimensions.

It will be appreciated by persons skilled in the art that the methods of the invention will typically be used to provide an initial diagnosis, for example to identify an individual at risk of having or developing pancreatic cancer, after which further clinical investigations (such as biopsy testing, in vivo imaging and the like) may be performed to confirm the diagnosis.

By “sample to be tested”, “test sample” or “control sample” we include a tissue or fluid sample taken or derived from an individual, wherein the sample comprises endogenous proteins and/or nucleic acid molecules and/or carbohydrate moieties. Preferably, the sample to be tested is provided from a mammal. Most preferably, the mammal is human.

The sample to be tested in the methods of the invention may be a cell, tissue or fluid sample (or derivative thereof) comprising or consisting of blood (fractionated or unfractionated), plasma, plasma cells, serum, tissue cells or equally preferred, protein or nucleic acid derived from a cell or tissue sample.

In one embodiment, the sample is a pancreatic tissue sample. In an alternative or additional embodiment, the sample is a sample of pancreatic cells.

Preferably, the sample may be a blood or serum sample.

In the methods of the invention, step (b) comprises or consists of measuring the presence and/or amount of one or more biomarker(s) listed in Table A, for example at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or all 13 of the biomarkers listed in Table A.

In a particular additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of six or more biomarker(s) listed in Table A, for example at least 7, 8, 9, 10, 11, 12 or all 13 of the biomarkers listed in Table A.

In the methods of the invention, step (b) comprises or consists of measuring the presence and/or amount of one or more biomarker(s) listed in Table A(i)-(vi), for example at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or all 12 of the biomarkers listed in Table A(i)-(vi).

In a particular additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of six or more biomarker(s) listed in Table A(i)-(vi), for example at least 7, 8, 9, 10, 11 or all 12 of the biomarkers listed in Table A(i)-(vi).

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of three or more biomarkers listed in Table A(i)-(v), for example at least 4, 5, 6, 7, 8, or 9 of the biomarkers listed in Table A(i)-(v).

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of one or more, e.g. 2, 3, 4, 5, 6, 7, or all 8 of the following biomarkers, and/or one or more secondary target(s) thereof: OPG, GSN, IGFBP3, Complement Factor B, MUC16, Complement C4, Complement C5, Cystatin C.

Optionally, the one or more secondary target(s) are selected from: VWF, HADH2, FCN2, MASP2.

By “secondary target(s)” we include any target bound by off-target binding, by the antibody or other binding agent, to one or more additional biomarker(s) other than the primary target biomarker (direct co-enrichment), and/or one or more additional biomarker(s) bound, by the antibody or other binding agent, due to interaction of the one or more additional biomarker(s) with the intended primary target biomarker (indirect co-enrichment). Note that the primary target does not need to be biologically or clinically more relevant than the secondary target(s).

For example, FCN2 and MASP2 are secondary targets of the MUC16 (1) clone as the scFv antibody directed against MUC16 was also shown to bind FCN2 and MASP2 (direct co-enrichment). As another example, VWF is a secondary target of the OPG (2) clone as it was bound by the same scFv antibody, which is expected to be due to the ability of OPG to form a complex with VWF (indirect co-enrichment). See Tables 3 and 4 for details of those antibodies and their sequences.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of the following biomarkers: OPG, GSN, IGFBP3, Complement Factor B, MUC16, Complement C4, Complement C5, and Cystatin C; and optionally, measuring the presence and/or amount of one or more additional biomarkers selected from: VWF, HADH2, FCN2, MASP2.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of all of the biomarkers listed in Table A (e.g. at the protein, mRNA and/or ctDNA level).

In an additional or alternative embodiment, step (b) comprises or consist of measuring the presence and/or amount of two or more of: OPG, VWF, GSN, IGFBP3, MUC16, FCN2, MASP2.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; and/or (ii) OPG and/or VWF.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) IGFBP3; and/or (iv) Complement Factor B.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) Complement Factor B; (iv) IGFBP3; (v) Complement C4; (vi) Complement C5; (vii) Cystatin C; and/or (viii) MUC16, FCN2 and/or MASP2.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; and (ii) OPG and/or VWF.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) IGFBP3; and (iv) Complement Factor B.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) Complement Factor B; (iv) IGFBP3; (v) Complement C4; (vi) Complement C5; (vii) Cystatin C; and (viii) MUC16, FCN2 and/or MASP2.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of VWF, FCN2, and/or MASP2.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of one or more of: OPG, VWF, GSN, IGFBP3, MUC16, FCN2 and/or MASP2.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of CA 19-9 (carbohydrate antigen 19-9). In particular, embodiments of any of the methods described herein may comprise measuring the presence and/or amount of CA 19-9 together with measuring the presence and/or amount of any combination of biomarkers listed in Table A(i)-(vi). CA 19-9 may be measured at the same or different time to the measurement of the other biomarkers.

In an additional or alternative embodiment, step (b) excludes measuring the presence and/or amount of CA 19-9 (carbohydrate antigen 19-9). In particular, embodiments of any of the methods described herein may exclude measuring the presence and/or amount of CA 19-9.

In an additional or alternative embodiment, step (b) excludes measuring the presence and/or amount of Complement Factor B. In particular, embodiments of any of the methods described herein may exclude measuring the presence and/or amount of Complement Factor B.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; and (iii) CA 19-9.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) Complement Factor B; and (iv) CA 19-9.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) IGFBP3; and (iv) CA 19-9.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) OPG and/or VWF; (ii) Complement Factor B; (iii) IGFBP3; and (iv) CA 19-9.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) IGFBP3; (iii) MUC16 and/or FCN2 and/or MASP2; and (iv) CA 19-9.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) Complement Factor B; (iv) IGFBP3; and (v) CA 19-9.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) Complement Factor B; (iv) IGFBP3; (v) Complement C5; and (vi) CA 19-9.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) Complement Factor B; (iv) IGFBP3; (v) Complement C5; (vi) MUC16 and/or FCN2 and/or MASP2; and (vii) CA 19-9.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) Complement Factor B; (iv) IGFBP3; (v) Complement C5; (vi) Cystatin C; (vii) MUC16 and/or FCN2 and/or MASP2; and (viii) CA 19-9.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) Complement Factor B; (iv) IGFBP3; (v) Complement C4; (vi) Complement C5; (vii) Cystatin C; (viii) MUC16 and/or FCN2 and/or MASP2; and (ix) CA 19-9.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) Complement Factor B; (iii) Complement C5; (iv) Cystatin C; (v) Complement C4; and (vi) CA 19-9.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) IGFBP3; (ii) Complement Factor B; (iii) Complement C5; (iv) Cystatin C; (v) Complement C4; and (vi) CA 19-9.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) MUC16 and/or FCN2 and/or MASP2; (ii) Complement Factor B; (iii) Complement C5; (iv) Cystatin C; (v) Complement C4; and (vi) CA 19-9.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) OPG and/or VWF; (ii) Complement Factor B; (iii) Complement C5; (iv) Cystatin C; (v) Complement C4; and (vi) CA 19-9.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) Complement Factor B; (ii) Complement C5; (iii) Cystatin C; (iv) Complement C4; and (v) CA 19-9.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; and (ii) OPG and/or VWF.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; and (iii) Complement Factor B.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; and (iii) IGFBP3.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) OPG and/or VWF; (ii) Complement Factor B; and (iii) IGFBP3.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) IGFBP3; and (iii) MUC16 and/or FCN2 and/or MASP2.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) Complement Factor B; and (iv) IGFBP3.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) Complement Factor B; (iv) IGFBP3; and (v) Complement C5.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) Complement Factor B; (iv) IGFBP3; (v) Complement C5; and (vi) MUC16 and/or FCN2 and/or MASP2.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) Complement Factor B; (iv) IGFBP3; (v) Complement C5; (vi) Cystatin C; and (vii) MUC16 and/or FCN2 and/or MASP2.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) Complement Factor B; (iv) IGFBP3; (v) Complement C4; (vi) Complement C5; (vii) Cystatin C; and (viii) MUC16 and/or FCN2 and/or MASP2.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) GSN and/or HADH2; (ii) Complement Factor B; (iii) Complement C5; (iv) Cystatin C; and (v) Complement C4.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) IGFBP3; (ii) Complement Factor B; (iii) Complement C5; (iv) Cystatin C; and (v) Complement C4.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) MUC16 and/or FCN2 and/or MASP2; (ii) Complement Factor B; (iii) Complement C5; (iv) Cystatin C; and (v) Complement C4.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of: (i) OPG and/or VWF; (ii) Complement Factor B; (iii) Complement C5; (iv) Cystatin C; and (v) Complement C4.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of one or more biomarker(s) listed in Table A, part (i) and/or part (ii).

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of one or more biomarker(s) listed in Table A, part (i) and/or part (iii) and/or part (v).

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of GSN and OPG.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of GSN, OPG, and IGFBP3.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of GSN, OPG, Complement Factor B, and IGFBP3.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of GSN, OPG, Complement Factor B, IGFBP3, Complement C4, Complement C5, Cystatin C, and MUC16.

In an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of GSN, OPG, IGFBP3, Complement C4, Complement C5, Cystatin C, and MUC16.

In an additional or alternative embodiment of any of the aspects of the invention described herein, in step (b) the presence and/or amount in the test sample of VWF is measured in addition to the presence and/or amount of OPG.

In an additional or alternative embodiment of any of the aspects of the invention described herein, in step (b) the presence and/or amount in the test sample of VWF is measured instead of the presence and/or amount of OPG. In an additional or alternative embodiment of each of the aspects of the invention described herein, in step (b) the presence and/or amount in the test sample of OPG is measured instead of VWF.

As detailed in Tables 3 and 5, the antibody sequence referred to herein as binding OPG may also bind VWF.

In an additional or alternative embodiment of any of the aspects of the invention described herein, measuring the presence and/or amount in the test sample of OPG and/or VWF in step (b) is replaced by measuring the presence and/or amount in the test sample of one or more protein bound by the antibody sequence of SEQ ID NO: 36. Preferably the protein bound by the antibody sequence of SEQ ID NO: 36 is OPG and/or VWF.

In an additional or alternative embodiment of any of the aspects of the invention described herein, in step (b) the presence and/or amount in the test sample of HADH2 is measured in addition to the presence and/or amount of GSN (gelsolin).

In an additional or alternative embodiment of any of the aspects of the invention described herein, in step (b) the presence and/or amount in the test sample of HADH2 is measured instead of the presence and/or amount of GSN. In an additional or alternative embodiment of each of the aspects of the invention described herein, in step (b) the presence and/or amount in the test sample of GSN is measured instead of HADH2.

As detailed in Tables 3 and 5, the antibody sequence referred to herein as binding GSN may also bindHADH2.

In an additional or alternative embodiment of any of the aspects of the invention described herein, measuring the presence and/or amount in the test sample of GSN and/or HADH2 in step (b) is replaced by measuring the presence and/or amount in the test sample of one or more protein bound by the antibody sequence of SEQ ID NO: 20. Preferably the protein bound by the antibody sequence of SEQ ID NO: 20 is GSN and/or HADH2.

In an additional or alternative embodiment of any of the aspects of the invention described herein, in step (b) the presence and/or amount in the test sample of FCN2 and/or MASP2 is measured in addition to the presence and/or amount of MUC16 (CA125).

In an additional or alternative embodiment of any of the aspects of the invention described herein, in step (b) the presence and/or amount in the test sample of FCN2 and/or MASP2 is measured instead of the presence and/or amount of MUC16. In an additional or alternative embodiment of each of the aspects of the invention described herein, in step (b) the presence and/or amount in the test sample of MUC16 is measured instead of FCN2 and/or MASP2.

As detailed in Table 3, the antibody sequence referred to herein as binding MUC16 may also bind FCN2 and/or MASP2.

In an additional or alternative embodiment of any of the aspects of the invention described herein, measuring the presence and/or amount in the test sample of MUC16, FCN2 and/or MASP2 in step (b) is replaced by measuring the presence and/or amount in the test sample of one or more protein bound by the antibody sequence of SEQ ID NO: 30. Preferably the protein bound by the antibody sequence of SEQ ID NO: 30 is MUC16, FCN2 and/or MASP2.

In an additional or alternative embodiment of any of the aspects of the invention described herein, measuring the presence and/or amount in the test sample of one or more biomarkers in step (b) is replaced by measuring the presence and/or amount in the test sample of one or more protein bound by one or more binding agent comprising one or more of the antibody sequences described in Table 5 and/or Table 6.

In an additional or alternative embodiment of any of the aspects of the invention described herein, measuring the presence and/or amount in the test sample of one or more biomarkers in step (b) is replaced by measuring the presence and/or amount in the test sample of one or more protein bound by one or more binding agents comprising one or more of the antibody sequences defined in Table 5, specifically one or more of SEQ ID NOs: 6, 11, 13, 15, 20, 30, 32, and 36.

In an additional or alternative embodiment, step (b) may comprise, consist of or exclude measuring the expression of OPG. Alternatively or additionally, step (b) comprises, consists of or excludes measuring the expression of VWF. Alternatively or additionally, step (b) comprises, consists of or excludes measuring the expression of GSN. Alternatively or additionally, step (b) comprises, consists of or excludes measuring the expression of HADH2. Alternatively or additionally, step (b) comprises, consists of or excludes measuring the expression of Complement Factor B. Alternatively or additionally, step (b) comprises, consists of or excludes measuring the expression of IGFBP3. Alternatively or additionally, step (b) comprises, consists of or excludes measuring the expression of Complement C4. Alternatively or additionally, step (b) comprises, consists of or excludes measuring the expression of Complement C5. Alternatively or additionally, step (b) comprises, consists of or excludes measuring the expression of Cystatin C. Alternatively or additionally, step (b) comprises, consists of or excludes measuring the expression of MUC16. Alternatively or additionally, step (b) comprises, consists of or excludes measuring the expression of FCN2. Alternatively or additionally, step (b) comprises, consists of or excludes measuring the expression of MASP2. Alternatively or additionally, step (b) comprises, consists of or excludes measuring the expression of CA19-9.

Thus, in an additional or alternative embodiment, step (b) comprises or consists of measuring the presence and/or amount of one or more biomarker listed in:

-   -   (i) Table A, part (i), for example both of the biomarkers listed         in Table A(i); and/or     -   (ii) Table A, part (ii), for example both of the biomarkers         listed in Table A(ii); and/or     -   (iii) Table A, part (iii); and/or     -   (iv) Table A, part (iv); and/or     -   (v) Table A, part (v), for example 2 or all of the biomarkers         listed in Table A(v); and/or     -   (vi) Table A, part (vi), for example 2 or all of the biomarkers         listed in Table A(vi); and/or     -   (vii) Table A, part (vii).

It will be appreciated that step (b) may additionally comprise measuring the presence and/or amount of one or more further biomarkers not listed in Table A, wherein the further biomarkers may provide additional diagnostic information.

For example, step (b) may comprise or consist of measuring the presence and/or amount of one or more biomarker(s) listed in Table 1.

For example, step (b) may comprise or consist of measuring the presence and/or amount of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, or all of the biomarkers in Table 1.

As another example, step (b) may comprise or consist of measuring the presence and/or amount of one or more biomarker(s) listed in Table 2.

In one preferred embodiment of the first aspect of the invention, step (b) comprises measuring the presence and/or amount of all of the biomarkers listed in Table A, e.g. at the protein level. Use of this biomarker signature allows the diagnosis of pancreatic cancer (i.e. PDAC) at any stage, including early stages of the disease, and including distinguishing from healthy individuals and individuals with symptoms suggestive or consistent with pancreatic cancer. Preferably, step (b) comprises measuring the presence and/or amount of CA19-9.

It will be appreciated by persons skilled in the art that, in addition to measuring the biomarkers in a sample from an individual to be tested, the methods of the invention may also comprise measuring those same biomarkers in one or more control samples.

Thus, in one embodiment, the method further comprises or consists of the steps of:

-   -   (c) providing one or more (negative) control samples from:         -   (i) an individual not afflicted with pancreatic cancer;             and/or         -   (ii) an individual afflicted with a benign pancreatic and/or             biliary disease, e.g. chronic pancreatitis or diabetes, e.g.             new-onset diabetes (e.g. type II); and     -   (d) determining a biomarker signature of the one or more control         samples by measuring the presence and/or amount in the control         sample of the one or more biomarkers measured in step (b);         wherein the pancreatic cancer-associated disease state is         identified in the event that the presence and/or amount in the         test sample of the one or more biomarkers measured in step (b)         is different from the presence and/or amount in the control         sample of the one or more biomarkers measured in step (d).

By “is different to the presence and/or amount in a control sample” we include that the presence and/or amount of the one or more biomarker(s) in the test sample differs from that of the one or more control sample(s) (or to predefined reference values representing the same). Preferably, the presence and/or amount in the test sample differs from the presence or amount in one or more control sample(s) (or mean of the control samples) by at least ±5%, for example, at least ±6%, ±7%, ±8%, ±9%, ±10%, ±11%, ±12%, ±13%, ±14%, ±15%, ±16%, ±17%, ±18%, ±19%, ±20%, ±21%, ±22%, ±23%, ±24%, ±25%, ±26%, ±27%, ±28%, ±29%, ±30%, ±31%, ±32%, ±33%, ±34%, ±35%, ±36%, ±37%, ±38%, ±39%, ±40%, ±41%, ±42%, ±43%, ±44%, ±45%, ±41%, ±42%, ±43%, ±44%, ±55%, ±60%, ±65%, ±66%, ±67%, ±68%, ±69%, ±70%, ±71%, ±72%, ±73%, ±74%, ±75%, ±76%, ±77%, ±78%, ±79%, ±80%, ±81%, ±82%, ±83%, ±84%, ±85%, ±86%, ±87%, ±88%, ±89%, ±90%, ±91%, ±92%, ±93%, ±94%, ±95%, ±96%, ±97%, ±98%, ±99%, ±100%, ±125%, ±150%, ±175%, ±200%, ±225%, ±250%, ±275%, ±300%, ±350%, ±400%, ±500% or at least ±1000% of the one or more control sample(s) (e.g., the negative control sample).

Alternatively or additionally, the presence or amount in the test sample differs from the mean presence or amount in the control samples by at least >1 standard deviation from the mean presence or amount in the control samples, for example, ≥1.5, ≥2, ≥3, ≥4, ≥5, ≥6, ≥7, ≥8, ≥9, ≥10, ≥11, ≥12, ≥13, ≥14 or ≥15 standard deviations from the mean presence or amount in the control samples. Any suitable means may be used for determining standard deviation (e.g., direct, sum of square, Welford's), however, in one embodiment, standard deviation is determined using the direct method (i.e., the square root of [the sum the squares of the samples minus the mean, divided by the number of samples]).

Alternatively or additionally, by “is different to the presence and/or amount in a control sample” we include that the presence or amount in the test sample does not correlate with the amount in the control sample in a statistically significant manner. By “does not correlate with the amount in the control sample in a statistically significant manner” we mean or include that the presence or amount in the test sample correlates with that of the control sample with a p-value of >0.001, for example, >0.002, >0.003, >0.004, >0.005, >0.01, >0.02, >0.03, >0.04 >0.05, >0.06, >0.07, >0.08, >0.09 or >0.1. Any suitable means for determining p-value known to the skilled person can be used, including z-test, t-test, Student's t-test, f-test, Mann-Whitney U test, Wilcoxon signed-rank test and Pearson's chi-squared test.

In an additional or alternative embodiment, a decrease in the amount of GSN and/or HADH2 measured in step (b) as compared to a negative control sample is indicative of the pancreatic cancer-associated disease state in the individual.

In an additional or alternative embodiment, a decrease in the amount of IGFBP3 measured in step (b) as compared to a negative control sample is indicative of the pancreatic cancer-associated disease state in the individual.

In an additional or alternative embodiment, a decrease in the amount of MUC16, FCN2 and/or MASP2 measured in step (b) as compared to a negative control sample is indicative of the pancreatic cancer-associated disease state in the individual.

In an additional or alternative embodiment, an increase in the amount of OPG and/or VWF measured in step (b) as compared to a negative control sample is indicative of the pancreatic cancer-associated disease state in the individual.

In an additional or alternative embodiment, an increase in the amount of Complement Factor B measured in step (b) as compared to a negative control sample is indicative of the pancreatic cancer-associated disease state in the individual.

In an additional or alternative embodiment, an increase in the amount of Complement C5 measured in step (b) as compared to a negative control sample is indicative of the pancreatic cancer-associated disease state in the individual.

In an additional or alternative embodiment, an increase in the amount of Complement C4 measured in step (b) as compared to a negative control sample is indicative of the pancreatic cancer-associated disease state in the individual.

In an additional or alternative embodiment, an increase in the amount of Cystatin C measured in step (b) as compared to a negative control sample is indicative of the pancreatic cancer-associated disease state in the individual.

In an additional or alternative embodiment, an increase in the amount of CA 19-9 measured in step (b) as compared to a negative control sample is indicative of the pancreatic cancer-associated disease state in the individual.

In one embodiment, the method of the invention may further comprise or consist of the steps of:

-   -   (e) providing one or more (positive) control sample from an         individual afflicted with pancreatic cancer; and     -   (f) determining a biomarker signature of the control sample by         measuring the presence and/or amount in the control sample of         the one or more biomarkers measured in step (b);         wherein the pancreatic cancer-associated disease state is         identified in the event that the presence and/or amount in the         test sample of the one or more biomarkers measured in step (b)         corresponds to the presence and/or amount in the control sample         of the one or more biomarkers measured in step (f).

Thus, the methods of the invention may comprise steps (c)+(d) and/or steps (e)+(f).

By “corresponds to the presence and/or amount in a control sample” we include that the presence and/or amount is identical to that of a positive control sample; or closer to that of one or more positive control sample than to one or more negative control sample (or to predefined reference values representing the same). Preferably the presence and/or amount is within ±40% of that of the one or more control sample (or mean of the control samples), for example, within ±39%, ±38%, ±37%, ±36%, ±35%, ±34%, ±33%, ±32%, ±31%, ±30%, ±29%, ±28%, ±27%, ±26%, ±25%, ±24%, ±23%, ±22%, ±21%, ±20%, ±19%, ±18%, ±17%, ±16%, ±15%, ±14%, ±13%, ±12%, ±11%, ±10%, ±9%, ±8%, ±7%, ±6%, ±5%, ±4%, ±3%, ±2%, ±1%, ±0.05% or within 0% of the one or more control sample (e.g., the positive control sample).

Alternatively or additionally, the difference in the presence or amount in the test sample is standard deviation from the mean presence or amount in the control samples, for example, ≥4.5, ≥4, ≥3.5, ≥3, ≥2.5, ≥2, ≥1.5, ≥1.4, ≥1.3, ≥1.2, ≥1.1, ≥1, ≥0.9, ≥0.8, ≥0.7, ≥0.6, ≥0.5, ≥0.4, ≥0.3, ≥0.2, ≥1 or 0 standard deviations from the from the mean presence or amount in the control samples, provided that the standard deviation ranges for differing and corresponding biomarker expressions do not overlap (e.g., abut, but no not overlap).

Alternatively or additionally, by “corresponds to the presence and/or amount in a control sample” we include that the presence or amount in the test sample correlates with the amount in the control sample in a statistically significant manner. By “correlates with the amount in the control sample in a statistically significant manner” we mean or include that the presence or amount in the test sample correlates with the that of the control sample with a p-value of ≥0.05, for example, ≥0.04, ≥0.03, ≥0.02, ≥0.01, ≥0.005, ≥0.004, ≥0.003, ≥0.002, ≥0.001, ≥0.0005 or ≥0.0001.

Differential expression (up-regulation or down regulation) of biomarkers, or lack thereof, can be determined by any suitable means known to a skilled person. Differential expression is determined to a p value of a least less than 0.05 (p=<0.05), for example, at least <0.04, <0.03, <0.02, <0.01, <0.009, <0.005, <0.001, <0.0001, <0.00001 or at least <0.000001. For example, differential expression may be determined using a support vector machine (SVM).

In one embodiment, the SVM is, or is derived from, the SVM script described in Table 7.

It will be appreciated by persons skilled in the art that differential expression may relate to a single biomarker or to multiple biomarkers considered in combination (i.e., as a biomarker signature). Thus, a p value may be associated with a single biomarker or with a group of biomarkers. Indeed, proteins having a differential expression p value of greater than 0.05 when considered individually may nevertheless still be useful as biomarkers in accordance with the invention when their expression levels are considered in combination with one or more other biomarkers.

As exemplified in the accompanying Example, the expression of certain proteins in a tissue, blood, serum or plasma test sample may be indicative of pancreatic cancer in an individual. For example, the relative expression of certain serum proteins in a single test sample may be indicative of the presence of pancreatic cancer in an individual.

In an alternative or additional embodiment, the presence and/or amount in the test sample of the one or more biomarkers measured in step (b) may be compared against predetermined reference values representative of the measurements in steps (d) and/or (f), i.e., reference negative and/or positive control values.

As detailed above, the methods of the invention may also comprise measuring, in one or more negative or positive control samples, the presence and/or amount of the one or more biomarkers measured in the test sample in step (b).

For example, one or more negative control samples may be from an individual who was not, at the time the sample was obtained, afflicted with:

-   -   (a) a pancreatic cancer, for example adenocarcinoma (e.g.,         pancreatic ductal adenocarcinoma or tubular papillary pancreatic         adenocarcinoma), pancreatic sarcoma, malignant serous         cystadenoma, adenosquamous carcinoma, signet ring cell         carcinoma, hepatoid carcinoma, colloid carcinoma,         undifferentiated carcinoma, and undifferentiated carcinomas with         osteoclast-like giant cells; and/or     -   (b) a non-cancerous pancreatic disease or condition, for example         acute pancreatitis, chronic pancreatitis and autoimmune         pancreatitis; and/or     -   (c) any other disease or condition.

Thus, the negative control sample may be obtained from a healthy individual.

Likewise, one or more positive control samples may be from an individual who, at the time the sample was obtained, was afflicted with a pancreatic cancer, for example adenocarcinoma (e.g., pancreatic ductal adenocarcinoma or tubular papillary pancreatic adenocarcinoma), pancreatic sarcoma, malignant serous cystadenoma, adenosquamous carcinoma, signet ring cell carcinoma, hepatoid carcinoma, colloid carcinoma, undifferentiated carcinoma, and undifferentiated carcinomas with osteoclast-like giant cells; and/or a non-cancerous pancreatic disease or condition, for example acute pancreatitis, chronic pancreatitis and autoimmune pancreatitis; and/or any other disease or condition.

In one preferred embodiment of the first aspect of the invention, the method is repeated on the individual. Thus, steps (a) and (b) may be repeated using a sample from the same individual taken at different time to the original sample tested (or the previous method repetition). Such repeated testing may enable disease progression to be assessed, for example to determine the efficacy of the selected treatment regime and (if appropriate) to select an alternative regime to be adopted.

Thus, in one embodiment, the method is repeated using a test sample taken between 1 day to 104 weeks to the previous test sample(s) used, for example, between 1 week to 100 weeks, 1 week to 90 weeks, 1 week to 80 weeks, 1 week to 70 weeks, 1 week to 60 weeks, 1 week to 50 weeks, 1 week to 40 weeks, 1 week to 30 weeks, 1 week to 20 weeks, 1 week to 10 weeks, 1 week to 9 weeks, 1 week to 8 weeks, 1 week to 7 weeks, 1 week to 6 weeks, 1 week to 5 weeks, 1 week to 4 weeks, 1 week to 3 weeks, or 1 week to 2 weeks.

Alternatively or additionally, the method may be repeated using a test sample taken every period from the group consisting of: 1 day, 2 days, 3 day, 4 days, 5 days, 6 days, 7 days, 10 days, 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, 8 weeks, 9 weeks, 10 weeks, 15 weeks, 20 weeks, 25 weeks, 30 weeks, 35 weeks, 40 weeks, 45 weeks, 50 weeks, 55 weeks, 60 weeks, 65 weeks, 70 weeks, 75 weeks, 80 weeks, 85 weeks, 90 weeks, 95 weeks, 100 weeks, 104, weeks, 105 weeks, 110 weeks, 115 weeks, 120 weeks, 125 weeks and 130 weeks.

Alternatively or additionally, the method may be repeated at least once, for example, 2 times, 3 times, 4 times, 5 times, 6 times, 7 times, 8 times, 9 times, 10 times, 11 times, 12 times, 13 times, 14 times, 15 times, 16 times, 17 times, 18 times, 19 times, times, 21 times, 22 times, 23, 24 times or 25 times.

Alternatively or additionally, the method is repeated continuously.

In one embodiment, the method is repeated until pancreatic cancer is diagnosed in the individual using the methods of the present invention and/or conventional clinical methods (i.e., until confirmation of the diagnosis is made).

Suitable conventional clinical methods are well known in the art. For example, those methods described in Ducreux et al., 2015, ‘Cancer of the pancreas: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up’ Annals of Oncology, 26 (Supplement 5): v56-v68 and/or Freelove & Walling, 2006, ‘Pancreatic Cancer: Diagnosis and Management’ American Family Physician, 73(3):485-492 which are incorporated herein by reference. Thus, the pancreatic cancer diagnosis may be confirmed using one or more method selected from the group consisting of computed tomography (preferably dual-phase helical computed tomography); transabdominal ultrasonography; endoscopic ultrasonography-guided fine-needle aspiration; endoscopic retrograde cholangio-pancreatography; positron emission tomography; magnetic resonance imaging; physical examination; and biopsy.

Alternatively and/or additionally, the pancreatic cancer diagnosis may be confirmed using known biomarker signatures for the diagnosis of pancreatic cancer. For example, the pancreatic cancer may be diagnosed with one or more biomarker or diagnostic method described in the group consisting of: WO 2008/117067 A9; WO 2012/120288 A2; WO 2015/067969 A2; WO 2017/050939 A2; WO 2017/194613 A2; and WO 2018/141804 A1.

In one preferred embodiment of the methods of the invention, step (a) comprises providing a serum sample from an individual to be tested and/or step (b) comprises measuring in the sample the expression of the protein or polypeptide of the one or more biomarker(s). Thus, a biomarker signature for the sample may be determined at the protein level.

In such an embodiment, step (b), (d) and/or step (f) may be performed using one or more first binding agents capable of binding to a biomarker (i.e., protein) listed in Table A. It will be appreciated by persons skilled in the art that the first binding agent may comprise or consist of a single species with specificity for one of the protein biomarkers or a plurality of different species, each with specificity for a different protein biomarker.

In an additional or alternative embodiment, one or more first binding agent(s) comprise(s) one or more of the antibody sequence(s) defined in Table 5 and/or Table 6.

In an additional or alternative embodiment, one or more first binding agent(s) comprise(s) one or more of the antibody sequences of SEQ ID NOs: 6, 11, 13, 15, 20, 30, 32, and 36.

In an additional or alternative embodiment, one or more first binding agent(s) comprise(s) one or more of the antibody sequences of SEQ ID NOs: 30, 32, and 36.

It will be appreciated that any combination of antibody sequences defined in Table 5 may be used in the methods of the invention, for example combinations correlating with each or any of the different combinations of biomarkers which may be measured as set out above.

Suitable binding agents (also referred to as binding molecules) can be selected from a library, based on their ability to bind a given target molecule, as discussed below.

In one preferred embodiment, at least one type of the binding agents, and more typically all of the types, may comprise or consist of an antibody or antigen-binding fragment of the same, or a variant thereof.

Methods for the production and use of antibodies are well known in the art, for example see Antibodies: A Laboratory Manual, 1988, Harlow & Lane, Cold Spring Harbor Press, ISBN-13: 978-0879693145, Using Antibodies: A Laboratory Manual, 1998, Harlow & Lane, Cold Spring Harbor Press, ISBN-13: 978-0879695446 and Making and Using Antibodies: A Practical Handbook, 2006, Howard & Kaser, CRC Press, ISBN-13: 978-0849335280 (the disclosures of which are incorporated herein by reference).

Thus, a fragment may contain one or more of the variable heavy (V_(H)) or variable light (V_(L)) domains. For example, the term antibody fragment includes Fab-like molecules (Better et al (1988) Science 240, 1041); Fv molecules (Skerra et al (1988) Science 240, 1038); single-chain Fv (scFv) molecules where the V_(H) and V_(L) partner domains are linked via a flexible oligopeptide (Bird et al (1988) Science 242, 423; Huston et al (1988) Proc. Natl. Acad. Sci. USA 85, 5879) and single domain antibodies (dAbs) comprising isolated V domains (Ward et al (1989) Nature 341, 544).

For example, the binding agent(s) may be scFv molecules.

The term “antibody variant” includes any synthetic antibodies, recombinant antibodies or antibody hybrids, such as but not limited to, a single-chain antibody molecule produced by phage-display of immunoglobulin light and/or heavy chain variable and/or constant regions, or other immunointeractive molecule capable of binding to an antigen in an immunoassay format that is known to those skilled in the art.

A general review of the techniques involved in the synthesis of antibody fragments which retain their specific binding sites is to be found in Winter & Milstein (1991) Nature 349, 293-299.

Molecular libraries such as antibody libraries (Clackson et al, 1991, Nature 352, 624-628; Marks et al, 1991, J Mol Biol 222(3): 581-97), peptide libraries (Smith, 1985, Science 228(4705): 1315-7), expressed cDNA libraries (Santi et al (2000) J Mol Biol 296(2): 497-508), libraries on other scaffolds than the antibody framework such as affibodies (Gunneriusson et al, 1999, Appl Environ Microbiol 65(9): 4134-40) or libraries based on aptamers (Kenan et al, 1999, Methods Mol Biol 118, 217-31) may be used as a source from which binding molecules that are specific for a given motif are selected for use in the methods of the invention.

Conveniently, the binding agent(s) may be immobilised on a surface (e.g., on a multiwell plate or array); see Example below.

In one embodiment of the methods of the invention, step (b), (d) and/or step (f) is performed using an assay comprising a second binding agent capable of binding to the one or more biomarkers, the second binding agent comprising a detectable moiety. For example, an immobilised (first) binding agent may initially be used to ‘trap’ the protein biomarker on to the surface of a microarray, and then a second binding agent may be used to detect the ‘trapped’ protein.

The second binding agent may be as described above in relation to the (first) binding agent, such as an antibody or antigen-binding fragment thereof.

It will be appreciated by skilled person that the one or more biomarkers (e.g., proteins) in the test sample may be labelled with a detectable moiety, prior to performing step (b). Likewise, the one or more biomarkers in the control sample(s) may be labelled with a detectable moiety.

Alternatively, or in addition, the first and/or second binding agents may be labelled with a detectable moiety.

By a “detectable moiety” we include the meaning that the moiety is one which may be detected and the relative amount and/or location of the moiety (for example, the location on an array) determined.

Suitable detectable moieties are well known in the art. For example, the detectable moiety may be selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety; an enzymatic moiety.

In one preferred embodiment, the detectable moiety is biotin.

Thus, the detectable moiety may be a fluorescent and/or luminescent and/or chemiluminescent moiety which, when exposed to specific conditions, may be detected. For example, a fluorescent moiety may need to be exposed to radiation (i.e., light) at a specific wavelength and intensity to cause excitation of the fluorescent moiety, thereby enabling it to emit detectable fluorescence at a specific wavelength that may be detected.

Alternatively, the detectable moiety may be an enzyme which is capable of converting a (preferably undetectable) substrate into a detectable product that can be visualised and/or detected. Examples of suitable enzymes are discussed in more detail below in relation to, for example, ELISA assays.

In a further alternative, the detectable moiety may be a radioactive atom which is useful in imaging. Suitable radioactive atoms include ^(99m)Tc and ¹²³I for scintigraphic studies. Other readily detectable moieties include, for example, spin labels for magnetic resonance imaging (MRI) such as ¹²³I again, ¹³¹I, ¹¹¹In, ¹⁹F, ¹³C, ¹⁵N, ¹⁷O, gadolinium, manganese or iron. Clearly, the agent to be detected (such as, for example, the one or more biomarkers in the test sample and/or control sample described herein and/or an antibody molecule for use in detecting a selected protein) must have sufficient of the appropriate atomic isotopes in order for the detectable moiety to be readily detectable.

Preferred assays for detecting serum or plasma proteins include enzyme linked immunosorbent assays (ELISA), radioimmunoassay (RIA), immunoradiometric assays (IRMA) and immunoenzymatic assays (IEMA), including sandwich assays using monoclonal and/or polyclonal antibodies. Exemplary sandwich assays are described by David et al in U.S. Pat. Nos. 4,376,110 and 4,486,530, hereby incorporated by reference. Antibody staining of cells on slides may be used in methods well known in cytology laboratory diagnostic tests, as well known to those skilled in the art.

Conveniently, the assay is an ELISA (Enzyme Linked Immunosorbent Assay) which typically involves the use of enzymes giving a coloured reaction product, usually in solid phase assays. Enzymes such as horseradish peroxidase and phosphatase have been widely employed. A way of amplifying the phosphatase reaction is to use NADP as a substrate to generate NAD which now acts as a coenzyme for a second enzyme system. Pyrophosphatase from Escherichia coli provides a good conjugate because the enzyme is not present in tissues, is stable and gives a good reaction colour. Chemiluminescent systems based on enzymes such as luciferase can also be used.

ELISA methods are well known in the art, for example see The ELISA Guidebook (Methods in Molecular Biology), 2000, Crowther, Humana Press, ISBN-13: 978-0896037281 (the disclosures of which are incorporated by reference).

Alternatively, conjugation with the vitamin biotin is frequently used since this can readily be detected by its reaction with enzyme-linked avidin or streptavidin to which it binds with great specificity and affinity.

In one preferred embodiment, step (b), (d) and/or step (f) may be performed using an array.

Arrays per se are well known in the art. Typically, they are formed of a linear or two-dimensional structure having spaced apart (i.e. discrete) regions (“spots”), each having a finite area, formed on the surface of a solid support. An array can also be a bead structure where each bead can be identified by a molecular code or colour code or identified in a continuous flow. Analysis can also be performed sequentially where the sample is passed over a series of spots each adsorbing the class of molecules from the solution. The solid support is typically glass or a polymer, the most commonly used polymers being cellulose, polyacrylamide, nylon, polystyrene, polyvinyl chloride or polypropylene. The solid supports may be in the form of tubes, beads, discs, silicon chips, microplates, polyvinylidene difluoride (PVDF) membrane, nitrocellulose membrane, nylon membrane, other porous membrane, non-porous membrane (e.g. plastic, polymer, perspex, silicon, amongst others), a plurality of polymeric pins, or a plurality of microtitre wells, or any other surface suitable for immobilising proteins, polynucleotides and other suitable molecules and/or conducting an immunoassay. The binding processes are well known in the art and generally consist of cross-linking covalently binding or physically adsorbing a protein molecule, polynucleotide or the like to the solid support. By using well-known techniques, such as contact or non-contact printing, masking or photolithography, the location of each spot can be defined. For reviews see Jenkins, R. E., Pennington, S. R. (2001, Proteomics, 2, 13-29) and Lal et al (2002, Drug Discov Today 15; 7(18 Suppl):S143-9).

Typically, the array is a microarray. By “microarray” we include the meaning of an array of regions having a density of discrete regions of at least about 100/cm², and preferably at least about 1000/cm². The regions in a microarray have typical dimensions, e.g., diameters, in the range of between about 10-250 μm, and are separated from other regions in the array by about the same distance. The array may also be a macroarray or a nanoarray.

Once suitable binding molecules (discussed above) have been identified and isolated, the skilled person can manufacture an array using methods well known in the art of molecular biology.

Examples of array formats are described below in the Example and references cited therein; e.g., see Steinhauer et al., 2002; Wingren and Borrebaeck, 2008; Wingren et al., 2005, Delfani et al., 2016 (the disclosure of which are incorporated herein by reference).

Thus, in an exemplary embodiment the method comprises:

-   -   (i) labelling biomarkers present in the sample (e.g., serum)         with biotin;     -   (ii) contacting the biotin-labelled proteins with an array         comprising a plurality of scFv immobilised at discrete locations         on its surface, the scFv having specificity for one or more of         the proteins in Table A(i)-(vi);     -   (iii) contacting the biotin-labelled proteins (immobilised on         the surface-bound scFv) with a streptavidin conjugate comprising         a fluorescent dye; and     -   (iv) detecting the presence of the dye at discrete locations on         the array surface         wherein the expression of the dye on the array surface is         indicative of the expression of a biomarker from Table A in the         sample.

In an additional or alternative embodiment, the presence and/or amount CA 19-9 is measured as part of the method. CA 19-9 may be measured together with the other biomarkers defined in Table A, or separately. For example, CA 19-9 may be measured via a separate ELISA method. The data for CA 19-9 may then be analysed together with or separately from the data for the other biomarkers defined in Table A in order to determine the pancreatic cancer-associated disease state.

In an alternative embodiment, step (b), (d) and/or (f) comprises measuring the expression of a nucleic acid molecule encoding the one or more biomarkers.

The nucleic acid molecule may be a gene expression intermediate or derivative thereof, such as a mRNA or cDNA.

Thus, measuring the expression of the one or more biomarker(s) in step (b), (d) and/or (f) may be performed using a method selected from the group consisting of Southern hybridisation, Northern hybridisation, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), quantitative real-time PCR (qRT-PCR), nanoarray, microarray, macroarray, autoradiography and in situ hybridisation.

For example, measuring the expression of the one or more biomarker(s) in step (b), (d) and/or (f) may be performed using one or more binding moieties, each individually capable of binding selectively to a nucleic acid molecule encoding one of the biomarkers identified in Table A.

Conveniently, the one or more binding moieties each comprise or consist of a nucleic acid molecule, such as DNA, RNA, PNA, LNA, GNA, TNA or PMO.

Advantageously, the one or more binding moieties are 5 to 100 nucleotides in length. For example, 15 to 35 nucleotides in length.

It will be appreciated that the nucleic acid-based binding moieties may comprise a detectable moiety.

Thus, the detectable moiety may be selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety (for example, a radioactive atom); or an enzymatic moiety.

Alternatively or additionally, the detectable moiety may comprise or consist of a radioactive atom, for example selected from the group consisting of technetium-99m, iodine-123, iodine-125, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, phosphorus-32, sulphur-35, deuterium, tritium, rhenium-186, rhenium-188 and yttrium-90.

Alternatively or additionally, the detectable moiety of the binding moiety may be a fluorescent moiety.

In a further embodiment, the nucleic acid molecule is a circulating tumour DNA molecule (ctDNA).

Methods suitable for detecting ctDNA are now well-established; for example, see Lewis et al., 2016, World J Gastroenterol. 22(32): 7175-7185, and references cited therein (the disclosures of which are incorporated herein by reference).

As detailed above, the sample provided in step (a) (and/or in step (c) and/or (e)) may be selected from the group consisting of unfractionated blood, plasma, serum, tissue fluid, pancreatic tissue, milk, bile and urine.

Conveniently, the sample provided in step (a), (c) and/or (e) is serum.

By appropriate selection of some or all of the biomarkers in Table A, optionally in conjunction with one or more further biomarkers, e.g. one or more additional biomarkers from Table 1, the methods of the invention exhibit high predictive accuracy for diagnosis of pancreatic cancer.

Thus, the predictive accuracy of the method, as determined by an ROC AUC value, may be at least 0.50, for example at least 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98 or at least 0.99.

Thus, in one embodiment, the predictive accuracy of the method, as determined by a ROC AUC value, is at least 0.80, or preferably at least 0.83.

Thus, in another embodiment, the predictive accuracy of the method, as determined by an ROC AUC value, is at least 0.90, or preferably at least 0.92.

In the methods of the invention, the ‘raw’ data obtained in step (b) (and/or in step (d) and/or (e)) undergoes one or more analysis steps before a diagnosis is reached. For example, the raw data may need to be standardised against one or more control values (i.e., normalised).

Typically, diagnosis is performed using a support vector machine (SVM), such as those available from http://cran.r-project.org/web/packages/e1071/index.html (e.g. e1071 1.5-24). However, any other suitable means may also be used.

Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other. Intuitively, an SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.

More formally, a support vector machine constructs a hyperplane or set of hyperplanes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. For more information on SVMs, see for example, Burges, 1998, Data Mining and Knowledge Discovery, 2:121-167.

In one embodiment of the invention, the SVM is ‘trained’ prior to performing the methods of the invention using biomarker profiles from individuals with known disease status (for example, individuals known to have pancreatic cancer, individuals known to have acute inflammatory pancreatitis, individuals known to have chronic pancreatitis or individuals known to be healthy). By running such training samples, the SVM is able to learn what biomarker profiles are associated with pancreatic cancer. Once the training process is complete, the SVM is then able to determine whether or not the biomarker sample tested is from an individual with pancreatic cancer.

However, this training procedure can be by-passed by pre-programming the SVM with the necessary training parameters. For example, diagnoses can be performed according to the known SVM parameters using the SVM algorithm detailed in Table 7, based on the measurement of any or all of the biomarkers listed in Table A.

It will be appreciated by skilled persons that suitable SVM parameters can be determined for any combination of the biomarkers listed in Table A by training an SVM machine with the appropriate selection of data (i.e. biomarker measurements from individuals with known pancreatic cancer status). Alternatively, the data of the Examples and figures may be used to determine a particular pancreatic cancer-associated disease state according to any other suitable statistical method known in the art.

Preferably, the method of the invention has an accuracy of at least 60%, for example 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% accuracy.

Preferably, the method of the invention has a sensitivity of at least 60%, for example 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sensitivity. The method of the invention may have a sensitivity of at least 83%.

Preferably, the method of the invention has a specificity of at least 60%, for example 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% specificity. The method of the invention may have a specificity of at least 95%.

By “accuracy” we mean the proportion of correct outcomes of a method, by “sensitivity” we mean the proportion of all pancreatic cancer positive sample that are correctly classified as positives, and by “specificity” we mean the proportion of all pancreatic cancer negative samples that are correctly classified as negatives.

Signal intensities may be quantified using any suitable means known to the skilled person, for example using Array-Pro (Media Cybernetics). Signal intensity data may be normalised (i.e., to adjust technical variation). Normalisation may be performed using any suitable method known to the skilled person. Alternatively or additionally, data are normalised using the empirical Bayes algorithm ComBat (Johnson et al., 2007).

Further statistical analysis of the refined data may be performed using methods well-known in the art, such as PCA, q-value calculation by ANOVA, and/or fold change calculation.

As described above, a first (‘training’) data set may be used to identify a combination of biomarkers, e.g. from Table A, to serve as a biomarker signature for the diagnosis of pancreatic cancer. Mathematical analysis of the training data set may be performed using known algorithms to determine the most suitable biomarker signatures. The predictive accuracy of a given biomarker combination (signature) can then be verified against a new (‘verification’) data set.

It will be appreciated by persons skilled in the art that the individual(s) tested may be of any ethnicity or geographic origin. Alternatively, the individual(s) tested may be of a defined sub-population, e.g., based on ethnicity and/or geographic origin. For example, the individual(s) tested may be Caucasian and/or Chinese (e.g., Han ethnicity).

Typically, the sample(s) provided in step (a), (c) and/or (e) are provided before treatment of the pancreatic cancer (e.g., resection, chemotherapy, radiotherapy).

In one embodiment, the individual(s) being tested suffers from one or more condition selected from the group consisting of chronic pancreatitis, hereditary pancreatic ductal adenocarcinoma and Peutz-Jeghers syndrome.

The pancreatic cancer to be diagnosed may be selected from the group consisting of adenocarcinoma, adenosquamous carcinoma, signet ring cell carcinoma, hepatoid carcinoma, colloid carcinoma, undifferentiated carcinoma, and undifferentiated carcinomas with osteoclast-like giant cells. Preferably, the pancreatic cancer is a pancreatic adenocarcinoma. More preferably, the pancreatic cancer is pancreatic ductal adenocarcinoma, also known as exocrine pancreatic cancer.

One preferred embodiment of the first aspect of the invention includes the additional step (g), following positive diagnosis of the individual with a pancreatic cancer, of providing the individual with pancreatic cancer therapy.

Thus, a related aspect of the invention provides a method of treatment of an individual with a pancreatic cancer comprising the following steps:

-   -   (a) diagnosing an individual as having a pancreatic cancer using         a method according to the first aspect of the invention; and     -   (b) treating the individual so diagnosed with a pancreatic         cancer therapy (for example, see Thota et al., 2014, Oncology         28(1):70-4, the disclosures of which are incorporated herein by         reference).

The pancreatic cancer therapy may be selected from the group consisting of surgery, chemotherapy, immunotherapy, chemoimmunotherapy, thermochemotherapy, radiotherapy and combinations thereof. For example, the pancreatic cancer therapy may be AC chemotherapy; Capecitabine and docetaxel chemotherapy (Taxotere®); CMF chemotherapy; Cyclophosphamide; EC chemotherapy; ECF chemotherapy; E-CMF chemotherapy (Epi-CMF); Eribulin (Halaven®); FEC chemotherapy; FEC-T chemotherapy; Fluorouracil (5FU); GemCarbo chemotherapy; Gemcitabine (Gemzar®); Gemcitabine and cisplatin chemotherapy (GemCis or GemCisplat); GemTaxol chemotherapy; Idarubicin (Zavedos®); Liposomal doxorubicin (DaunoXome®); Mitomycin (Mitomycin C Kyowa®); Mitoxantrone; MM chemotherapy; MMM chemotherapy; Paclitaxel (Taxol®); TAC chemotherapy; Taxotere and cyclophosphamide (TC) chemotherapy; Vinblastine (Velbe®); Vincristine (Oncovin®); Vindesine (Eldisine®); and Vinorelbine (Navelbine®).

Accordingly, a further aspect of the invention provides an antineoplastic agent (or combination thereof) for use in treating pancreatic cancer wherein the dosage regime thereof is determined based on the results of the method of the first aspect of the invention.

A related aspect of the invention provides the use of an antineoplastic agent (or combination thereof) in treating pancreatic cancer wherein the dosage regime thereof is determined based on the results of the method of the first aspect of the invention.

A further related aspect of the invention provides the use of an antineoplastic agent (or combination thereof) in the manufacture of a medicament for treating pancreatic cancer wherein the dosage regime thereof is determined based on the results of the method of the first aspect of the invention.

Thus, the present invention also provides a method of treating pancreatic cancer comprising administering to a patient an effective amount of an antineoplastic agent (or combination thereof) wherein the amount of antineoplastic agent (or combination thereof) effective to treat the pancreatic cancer is determined based on the results of the method of the first aspect of the invention.

In one embodiment, the antineoplastic agent comprises or consists of an alkylating agent (ATC code L01a), an antimetabolite (ATC code L01b), a plant alkaloid or other natural product (ATC code L01c), a cytotoxic antibiotic or a related substance (ATC code L01d), or another antineoplastic agent (ATC code L01x).

Hence, in one embodiment the antineoplastic agent comprises or consists of an alkylating agent selected from the group consisting of a nitrogen mustard analogue (for example cyclophosphamide, chlorambucil, melphalan, chlormethine, ifosfamide, trofosfamide, prednimustine or bendamustine) an alkyl sulfonate (for example busulfan, treosulfan, or mannosulfan) an ethylene imine (for example thiotepa, triaziquone or carboquone) a nitrosourea (for example carmustine, lomustine, semustine, streptozocin, fotemustine, nimustine or ranimustine) an epoxides (for example etoglucid) or another alkylating agent (ATC code L01ax, for example mitobronitol, pipobroman, temozolomide or dacarbazine).

In another embodiment the antineoplastic agent comprises or consists of an antimetabolite selected from the group consisting of a folic acid analogue (for example methotrexate, raltitrexed, pemetrexed or pralatrexate), a purine analogue (for example mercaptopurine, tioguanine, cladribine, fludarabine, clofarabine or nelarabine) or a pyrimidine analogue (for example cytarabine, fluorouracil (5-FU), tegafur, carmofur, gemcitabine, capecitabine, azacitidine or decitabine).

In a still further embodiment the antineoplastic agent comprises or consists of a plant alkaloid or other natural product selected from the group consisting of a vinca alkaloid or a vinca alkaloid analogue (for example vinblastine, vincristine, vindesine, vinorelbine or vinflunine), a podophyllotoxin derivative (for example etoposide or teniposide) a colchicine derivative (for example demecolcine), a taxane (for example paclitaxel, docetaxel or paclitaxel poliglumex) or another plant alkaloids or natural product (ATC code L01cx, for example trabectedin).

In one embodiment the antineoplastic agent comprises or consists of a cytotoxic antibiotic or related substance selected from the group consisting of an actinomycine (for example dactinomycin), an anthracycline or related substance (for example doxorubicin, daunorubicin, epirubicin, aclarubicin, zorubicin, idarubicin, mitoxantrone, pirarubicin, valrubicin, amrubicin or pixantrone) or another (ATC code L01dc, for example bleomycin, plicamycin, mitomycin or ixabepilone).

In a further embodiment the antineoplastic agent comprises or consists of an antineoplastic agent selected from the group consisting of a platinum compound (for example cisplatin, carboplatin, oxaliplatin, satraplatin or polyplatillen) a methylhydrazine (for example procarbazine) a monoclonal antibody (for example edrecolomab, rituximab, trastuzumab, alemtuzumab, gemtuzumab, cetuximab, bevacizumab, panitumumab, catumaxomab or ofatumumab) a sensitizer used in photodynamic/radiation therapy (for example porfimer sodium, methyl aminolevulinate, aminolevulinic acid, temoporfin or efaproxiral) or a protein kinase inhibitor (for example imatinib, gefitinib, erlotinib, sunitinib, sorafenib, dasatinib, lapatinib, nilotinib, temsirolimus, everolimus, pazopanib, vandetanib, afatinib, masitinib or toceranib).

In a still further embodiment the antineoplastic agent comprises or consists of an antineoplastic agent selected from the group consisting of amsacrine, asparaginase, altretamine, hydroxycarbamide, lonidamine, pentostatin, miltefosine, masoprocol, estramustine, tretinoin, mitoguazone, topotecan, tiazofurine, irinotecan (camptosar), alitretinoin, mitotane, pegaspargase, bexarotene, arsenic trioxide, denileukin diftitox, bortezomib, celecoxib, anagrelide, oblimersen, sitimagene ceradenovec, vorinostat, romidepsin, omacetaxine mepesuccinate, eribulin or folinic acid.

In one embodiment the antineoplastic agent comprises or consists of a combination of one or more antineoplastic agent, for example, one or more antineoplastic agent defined herein. One example of a combination therapy used in the treatment of pancreatic cancer is FOLFIRINOX which is made up of the following four drugs:

-   -   FOL—folinic acid (leucovorin);     -   F—fluorouracil (5-FU);     -   IRIN—irinotecan (Camptosar); and     -   OX—oxaliplatin (Eloxatin).

Thus, by combining certain optional embodiments from the above-described methods, the invention may provide a method for diagnosing and treating pancreatic adenocarcinoma (e.g. stage I or II) in an individual, said method comprising:

-   -   (a) obtaining or providing a serum or plasma sample for a human         patient;     -   (b) detecting the presence and/or amount of one or more (e.g.         all) of the biomarkers from Table A in the sample (e.g. by         contacting the sample with one or more antibodies, or         antigen-binding fragments thereof, each having specificity for         one of the biomarkers and detecting binding of said antibodies         or fragments to said biomarkers);     -   (c) diagnosing the patient with pancreatic adenocarcinoma (e.g.         stage I or II) based on the presence and/or amount of the one or         more biomarkers in the sample; and     -   (d) administering an effective amount of a chemotherapeutic         agent (e.g. gemcitabine) to the diagnosed patient and/or         surgically removing the pancreas, in whole or in part, and/or         administering radiotherapy.

It will be appreciated that step (b) may, for example, comprise determining the presence and/or amount in the sample of two or more, e.g. all, of the biomarkers listed in Table A. This step may comprise the use of an array, as described herein, e.g. comprising a plurality of scFvs, having specificity towards the biomarkers, immobilised on the surface of an array plate.

In an additional or alternative embodiment of any aspect of the invention, the presence and/or amount of CA 19-9 may be determined by a separate method, e.g. via an enzyme linked immunosorbent assay (ELISA) kit.

It will be appreciated that step (c) may comprise one or more further clinical investigations (such as testing a biopsy sample and/or in vivo imaging of the patient) in order to confirm or establish the diagnosis.

It will be appreciated that step (d) may comprise administration of combinations of chemotherapeutic agent and/or surgery and/or radiotherapy.

In one preferred embodiment, the patient is diagnosed with resectable pancreatic adenocarcinoma (e.g. stage I or II) and step (d) comprises surgical removal of the pancreas in whole or in part (e.g. using the Whipple procedure to remove the pancreas head or a total pancreatectomy) combined with chemotherapy (e.g. gemcitabine and/or 5-fluorouracil). It will be appreciated that the chemotherapy may be administered before and/or after the surgery.

In one embodiment, such methods permit the diagnosis of early stage pancreatic adenocarcinoma prior to the phenotypic presentation of the disease (i.e. before observable clinical symptoms develop). Thus, the methods may be used to diagnose pancreatic adenocarcinoma in asymptomatic patients, especially those at high risk of developing pancreatic cancer such as those with a family history of the disease, tobacco smokers, obese individuals, diabetics, and individuals with a chronic pancreatitis, chronic hepatitis B infection, cholelithiasis and/or an associated genetic predisposition (e.g. Peutz-Jeghers syndrome, familial atypical multiple mole melanoma syndrome, Lynch syndrome, BRCA1 mutations and/or BRCA2 mutations). Effective monitoring of such high-risk individuals can enable early diagnosis of pancreatic adenocarcinoma and so greatly increase the chances of survival.

Another aspect of the invention provides a method for treating a pancreatic cancer-associated disease state in a subject comprising or consisting of administering a pancreatic cancer therapy to a subject, wherein said subject has a biomarker signature of the present invention indicating the presence of the pancreatic cancer-associated disease state in the subject. The pancreatic cancer therapy may be resection, chemotherapy, and/or radiotherapy. In one embodiment, the pancreatic cancer therapy comprises the administration of at least one antineoplastic agent, as described hereinabove.

The method may further comprise (e.g. prior to treatment) measuring the presence and/or amount in a test sample of one or more biomarkers selected from the group defined in Table A (e.g. all the biomarkers in Table A). The method may comprise determining a biomarker signature of a test sample from the subject (e.g. prior to treatment), as described hereinabove.

Another aspect of the invention provides a method for detecting a biomarker signature of clinical significance (e.g. of diagnostic and/or prognostic value) in or of a biological sample (e.g. a serum sample), the method comprising steps (a) and (b) as defined above in relation to the first aspect of the invention. Preferably, the biomarker signature comprises or consists of all of the biomarkers in Table A.

A further aspect of the invention related to the first aspect of the invention provides a method for diagnosing or determining a pancreatic cancer-associated disease state comprising or consisting of the steps of:

-   -   (a) providing a sample from an individual to be tested; and     -   (b) determining a biomarker signature of the test sample by         measuring the presence and/or amount in the test sample of one         or more protein(s) bound by one or more of the antibody         sequence(s) described in Table 5 and/or Table 6;         wherein the presence and/or amount in the test sample of the one         or more protein(s) bound by one or more of the antibody         sequence(s) described in Table 5 and/or Table 6 is indicative of         the pancreatic cancer-associated disease state in the         individual.

In an alternative or additional embodiment, step (b) comprises measuring the presence and/or amount in the test sample of one or more protein(s) bound by one or more of the antibody sequence(s) described in Table 5. For example, one or more protein(s) bound by one or more binding agent(s) comprising one or more of SEQ ID NO: 6, 11, 13, 15, 20, 30, 32, and 36.

All embodiments described in relation to the other aspects of the invention herein are equally applicable to this further aspect of the invention. Furthermore, this aspect of the invention may be combined with any embodiment of the first aspect of the invention. For example, the method may comprise, in step (b), measuring the presence and/or amount in the test sample of two or more biomarkers from Table A, together with measuring the presence and/or amount in the test sample of one or more protein(s) bound by one or more binding agent(s) comprising one or more of the antibody sequence(s) described in Table 5.

In one embodiment, the method comprises measuring the presence and/or amount in the test sample of one or more protein(s) bound by one or more binding agents comprising one or more of the antibody sequence(s) SEQ ID NO: 30, 32, and 36.

A further aspect of the invention provides an array for diagnosing or determining a pancreatic cancer-associated disease state in an individual comprising an agent or agents (such as any of the above-described binding agents) for detecting the presence in a sample of one or more of the biomarkers defined in Table A(i)-(vi), optionally additionally comprising an agent for detecting the presence of CA 19-9.

Thus, the array is suitable for performing a method according to the first or a subsequent aspect of the invention.

The array comprises one or more binding agents capable (individually or collectively) of binding to one or more of the biomarkers defined in Table A, either at the protein level or the nucleic acid level.

The array may comprise one or more, preferably two or more, binding agents, wherein the binding agents are each capable of binding selectively to a biomarker as defined in the first aspect. Therefore, the array may comprise or consist of a particular selection of biomarker-specific binding agents which correlates to any particular selection of biomarkers as defined in the first aspect.

In one preferred embodiment, the array comprises one or more antibodies, or antigen-binding fragments thereof, capable (individually or collectively) of binding to one or more of the biomarkers defined in Table A at the protein level. For example, the array may comprise scFv molecules capable (collectively) of binding to all of the biomarkers defined in Table A(i)-(vi) at the protein level, optionally the array may additionally comprise an agent for binding to CA 19-9.

In an alternative embodiment, the array comprises one or more antibodies, or antigen-binding fragments thereof, capable (individually or collectively) of binding to the following biomarkers:

-   -   (i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) Complement         Factor B; (iv) IGFBP3; (v) Complement C4; (vi) Complement         C5; (vii) Cystatin C; and (viii) MUC16, FCN2 and/or MASP2; and         optionally one or more additional biomarkers from Table 1.

In an additional or alternative embodiment, the array comprises one or more antibodies, or antigen-binding fragments thereof, capable of binding to CA 19-9. However, CA 19-9 may be measured separately.

It will be appreciated that the array may comprise one or more positive and/or negative control samples. For example, conveniently the array comprises bovine serum albumin as a positive control sample and/or phosphate-buffered saline as a negative control sample.

Conveniently, the array comprises one or more, e.g. all, of the antibodies in Table 5.

Advantageously, the array comprises one or more, e.g. all, of the antibodies in Table 6.

A further aspect of the invention provides use of one or more biomarkers selected from the group defined in Table A as a biomarker for determining a pancreatic cancer associated disease states in an individual.

In an additional or alternative embodiment, the use comprises the following biomarkers:

(i) GSN and/or HADH2; (ii) OPG and/or VWF; (iii) Complement Factor B; (iv) IGFBP3; (v) Complement C4; (vi) Complement C5; (vii) Cystatin C; and (viii) MUC16 and/or FCN2 and/or MASP2; optionally including CA 19-19 and/or one or more additional biomarkers from Table 1.

For example, all of the biomarkers (e.g. proteins) defined in Table A may be used together as a diagnostic signature for determining the presence of pancreatic cancer in an individual.

A further aspect of the invention provides a kit for diagnosing or determining a pancreatic cancer-associated disease state in an individual comprising:

-   -   (a) an array according to the invention, or components for         making the same; and     -   (b) instructions for performing the method as defined above         (e.g., in the first or subsequent aspects of the invention).

A further aspect of the invention provides a use of one or more binding moieties to a biomarker as described herein (e.g. in Table A or specifically in Table A(i)-(vi)) in the preparation of a kit for diagnosing or determining a pancreatic cancer-associated disease state in an individual. Thus, multiple different binding moieties may be used, each targeted to a different biomarker, in the preparation of such as kit. In one embodiment, the binding moiety is an antibody or antigen-binding fragment thereof (e.g. scFv), as described herein.

A further aspect of the invention provides a method of treating pancreatic cancer in an individual comprising the steps of:

-   -   (a) determining a pancreatic cancer associated disease state         according to the method defined in any the first or subsequent         aspects of the invention; and     -   (b) providing the individual with pancreatic cancer therapy.

For example, the pancreatic cancer therapy may be selected from the group consisting of surgery (e.g., resection), chemotherapy, immunotherapy, chemoimmunotherapy and thermochemotherapy (see above).

A further aspect of the invention provides a computer program for operating the methods the invention, for example, for interpreting the expression data of step (c) (and subsequent expression measurement steps) and thereby diagnosing or determining a pancreatic cancer-associated disease state. The computer program may be a programmed SVM. The computer program may be recorded on a suitable computer-readable carrier known to persons skilled in the art. Suitable computer-readable-carriers may include compact discs (including CD-ROMs, DVDs, Blu-ray and the like), floppy discs, flash memory drives, ROM or hard disc drives. The computer program may be installed on a computer suitable for executing the computer program.

Preferred, non-limiting examples which embody certain aspects of the invention will now be described, with reference to the following figures:

FIG. 1 : Volcano plot for the 8-plex biomarker signature

The plot shows inverse unadjusted log 10 P-values plotted against log 2 fold change ratios (PDAC vs. controls) for the eight proteins included in the selected biomarker signature. Notice that the HADH2 (3) scFv clone mainly reflects the differential expression of gelsolin (GSN), and that the OPG (2) and MUC16 (1) clones may also reflect differential expression of the VWF and FCN2/MASP2, respectively.

FIG. 2 : Classification of healthy controls vs. PDAC stage I-IV

Classification concerns healthy controls from patient samples of different PDAC stages. Data have been derived by the 8-plex microarray-based analysis in combination with CA19-9 ELISA. Results are presented as a ROC-curve with its corresponding AUC-value.

FIG. 3 : Classification of symptomatic controls vs. PDAC stage I-IV

Classification concerns symptomatic controls from patient samples of different PDAC stages. Data have been derived by the 8-plex microarray-based analysis in combination with CA19-9 ELISA. Results are presented as a ROC-curve with its corresponding AUC-value.

FIG. 4 : Classification of all controls vs. PDAC stage I-IV

Classification of all controls (healthy and symptomatic) vs. patient samples of different PDAC stages. Data have been derived by the 8-plex microarray-based analysis in combination with CA19-9 ELISA. In total, 1113 samples were analyzed. The analysis yields a SVM ROC AUC-value of 0.94 differentiating PDAC from all controls.

FIG. 5 : Classification of all controls vs. PDAC stage I/II

Classification concerns all controls (healthy and symptomatic) from patient samples of early PDAC stages (I/II). Data have been derived by the 8-plex microarray-based analysis in combination with CA19-9 ELISA. The analysis yields a SVM ROC AUC-value of 0.95 differentiating 89 PDAC Stages I & II from all controls.

FIG. 6 : Classification of chronic pancreatitis vs. PDAC stage I/II

Classification concerns chronic pancreatitis controls (a subgroup of the symptomatic controls) from patient samples of early PDAC stages (I/II). Data have been derived by the 8-plex microarray-based analysis in combination with CA19-9 ELISA. Results are presented as a ROC-curve with its corresponding AUC-value.

FIG. 7 : Classification of PDAC stage I-IV vs. familial/hereditary controls

Classification of serum samples from patients with PDAC (all stages) vs. familial/hereditary controls in the blinded validation. Data have been derived by multiplex microarray-based analysis in combination with CA19-9 assessment. The analysis yields a ROC AUC-value of 0.94 differentiating PDAC samples (stages I-IV; n=167) from the familial/hereditary controls (n=203).

FIG. 8 : Classification of PDAC stage I/II vs. familial/hereditary controls

Classification of serum samples from patients with early stage (stage I/II) PDAC vs. familial/hereditary controls in the blinded validation. Data have been derived by multiplex microarray-based analysis in combination with CA19-9 assessment. The analysis yields a ROC AUC-value of 0.92 differentiating early stage PDAC samples (stages I/II; n=56) from the familial/hereditary controls (n=203).

FIG. 9 : Classification of PDAC stage I-IV vs. healthy controls

Classification of serum samples from patients with PDAC (all stages) vs. healthy controls in the blinded validation. Data have been derived by multiplex microarray-based analysis in combination with CA19-9 assessment. The analysis yields a ROC AUC-value of 0.95 differentiating PDAC samples (stages I-IV; n=167) from the healthy controls (n=221).

FIG. 10 : Classification of PDAC stage I-II vs. healthy controls

Classification of serum samples from patients with early stage (stage I/II) PDAC vs. healthy controls in the blinded validation. Data have been derived by multiplex microarray-based analysis in combination with CA19-9 assessment. The analysis yields a ROC AUC-value of 0.93 differentiating early stage PDAC samples (stages I/II; n=56) from the healthy controls (n=221).

FIG. 11 : Distribution of mean decision values

Distribution of mean decision values for PDAC (all stages) samples, hereditary/FPC risk group samples (designated PanFam) and healthy controls, respectively, in the blinded validation. The plot shows the frequency of individuals from each of the three subgroups binned into decision values intervals of 0.25.

EXAMPLE 1 Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive malignancies with a 5-year survival rate of less than 10%. Diffuse symptoms and lack of biomarkers for early detection often results in late-stage diagnosis and explains the high mortality rate. Biomarker panels that enable earlier PDAC detection would have great value in improving patient management and survival rate.

Methods

A recombinant antibody microarray platform was utilized to decipher a biomarker signature associated with PDAC. Initially, 396 single-chain variable fragments (scFvs) directed against 183 unique targets were assessed for their ability to discriminate PDAC from non-PDAC controls. Following data mining, 38 scFvs were selected to be included in a subsequent multicenter study. The 38-plex microarray was used to probe a serum sample set (n=1113) consisting of 315 PDAC (stage I-IV), 310 healthy controls and 488 symptomatic controls. Samples had been collected from patients and healthy volunteers at seven reference sites in USA and Europe.

Results

Biostatistical analysis of microarray data was used to derive a novel 8-plex biomarker signature for detection of early stage PDAC. When combined with a CA19-9 assay, the signature panel was able to detect early PDAC (stage I and II) versus all controls (healthy and symptomatic) with a ROC-AUC of 0.95.

Conclusion

A novel 8-plex biomarker signature adds significant orthogonal information to the established CA19-9 biomarker to allow detection of early stage PDAC with high predictive performance. This clearly points to the possibility of earlier diagnosis of pancreatic cancer and thereby an increased rate of surgically resectable tumors.

Abbreviations

AUC, Area under the curve; CV, Coefficient of variance; ELISA, enzyme linked immunosorbent assay; IP-MS, Immunoprecipitation mass spectrometry; LASSO, Least absolute shrinkage and selection operator; MWCO, Molecular weight cut-off; MT-PBS, PBS with 1% milk and 1% Tween-20; PBS, Phosphate buffered saline; PDAC, Pancreatic ductal adenocarcinoma; RF, Random forest; ROC, Receiver operating characteristic; RT, Room temperature; scFv, Single-chain fragment variable; SVM, Support vector machine; T-PBS, PBS with 0.05% Tween-20

Introduction

In this multicenter case-control study, serum from PDAC stage I-IV patients were analyzed using Immunovia's IMMray™ recombinant antibody microarray platform, in combination with a CA19-9 assay, to identify a biomarker signature able to discriminate stage I/II pancreatic cancer from clinically relevant controls that are presenting at the secondary care with alarming symptoms overlapping early PDAC.

Patients may be symptomatic for at least 12 months before a diagnosis of pancreatic cancer is made (Haeno et al, 2012, Hippisley-Cox and Coupland, 2012 Stapley et al, 2012). A recent study from University College London (UCL) interrogated a large UK primary care database, The Health Improvement Network (THIN). This study showed that PDAC patients in average visited the general practitioner three times more often the year prior to diagnosis, with symptoms such as abdominal pain (390/s), jaundice (36%), change in bowel habit (30%) or dyspepsia (21%) (Keane et al, 2014i, Keane et al, 2014ii). Currently, patients with suspected pancreatic cancer are investigated by numerous referral pathways with generally poor efficacy and outcomes.

By collecting and subsequently using symptomatic control samples that properly reflect the clinical population of concern, we aimed to maximize the clinical translatability of the results. Studies based solely on comparisons with healthy controls may lead to biomarker signatures that are non-specific to the disease of concern, and to sensitivity and specificity estimates that are unlikely to represent the performance of the test in the intended clinical setting.

Methods Sample Collection

All serum samples were collected between 2016 and 2019 at seven pancreatic disease reference sites in USA and Europe. The sites contributing with samples in this study were: UCL Institute for Liver and Digestive Health, London UK; University of Pittsburgh, Division of Gastroenterology, Hepatology & Nutrition, Pittsburgh, USA; New York University Langone Health, Perlmutter Cancer Center, New York, USA; Beth Israel Deaconess Medical Center (BIDMC), Pancreas and Liver Institute, Boston, USA; Ramon y Cajal Institute for Health Research (IRYCIS), Madrid, Spain; Växjö Central Hospital, Department of Transfusion Medicine, Sweden and Hallands Hospital Varberg, Department of Transfusion Medicine, Sweden.

Demographics of Study Cohorts

The samples from UCL comprised 30 PDAC, 79 diabetic controls and 409 non-diabetic symptomatic controls. The samples from University of Pittsburgh comprised 169 PDAC and 15 healthy controls. The samples from IRYCIS comprised 44 PDAC and 50 healthy controls. 60 PDAC samples were from the Perlmutter Cancer Center and 12 PDAC samples were from BIDMC. Finally, 141 healthy controls were from the hospital in Växjö, 48 were from the hospital in Varberg and 56 from a commercial source (Folio Conversant).

The symptomatic control samples were collected from individuals having concerning symptoms suggestive of PDAC (e.g. abdominal pain and jaundice), but that were subsequently diagnosed with various benign pancreatic and biliary diseases, e.g. acute and chronic pancreatitis, liver disease, pancreatic cyst, gallstone disease and IgG4 disease.

CA19-9 Assay

Serum CA19-9 levels were determined using an enzyme linked immunosorbent assay (ELISA) kit (CanAg CA19-9 EIA, Fujirebio Diagnostics, Göteborg, Sweden). The level of CA19-9 were calculated by interpolation from a reference curve generated in the same assay with reference standards of known concentrations. All CA19-9 assays were performed in duplicate according to the manufacturer's instructions.

Sample Biotinylation

The serum samples were labeled with biotin. Briefly, the serum was diluted 1:9 in PBS to a total protein concentration of approximately 8 g/I and labeled with 1.1 mM EZ-Link NHS-PEG4-Biotin (Thermo Fisher Scientific). The solution was allowed to react for two hours at 4° C. and then quenched with the addition of 0.5M Tris-HCl, pH 8.0. The biotinylated serum samples were aliquoted and stored at −20° C. until further analysis.

Antibody Microarray Production

The antibody microarrays consisted of 38 human recombinant scFvs directed against 33 different antigens (Table 1).

The scFv antibodies have been selected from large phage display libraries using stringent screening and selection protocols, and are all based on a scaffold that has been shown to provide favourable antigen-binding properties and high on-chip functionality (Steinhauer et al., 2002, Säll et al., 2016). In addition, the specificity of selected antibodies has been validated using pure proteins, mixtures of pure proteins, as well as serum samples with i) known levels of the targeted analyte(s), ii) spiked with known level of specific protein(s), and/or iii) depleted of the targeted protein(s). Orthogonal methods such as mass spectrometry (affinity pull-down experiments), ELISA, Meso Scale Discovery (MSD) cytokine assay, cytometric bead assay, as well as spiking and blocking experiments, have also been utilized for assessing antibody specificities (Söderlind et al., 2000; Ingvarsson et al., 2007, Wingren and Borrebaeck, 2008, Borrebaeck and Wingren, 2011; Carlsson et al., 2011).

His-tagged scFvs were produced in the periplasm of E. coli and purified by immobilized metal ion affinity chromatography (IMAC) using His MultiTrap HP 96-well filter plates (GE Healthcare Life Science). The elution buffer was exchanged for PBS, using Zeba™ Spin desalting 96-well spin plates with 7K MWCO resins (Thermo Fisher Scientific). Protein concentration was estimated using a Pierce Coomassie (Bradford) protein assay kit (Thermo Fisher Scientific). Protein purity was assessed by SDS-PAGE using 8-16% Criterion TGX Stain-Free gels (Bio-Rad). Antibody microarrays were produced on black MaxiSorp slides (Nunc, Thermo Fisher Scientific), using a non-contact printer (sciFLEXARRAYER SX; Scienion). The array layout was printed using spot-on-the-fly option and with one PDC (glass piezo dispense capillary). 14 identical arrays were printed on each slide in two columns of seven arrays.

Each array consisted of 14×18 spots with 300 μm spot-to-spot center distance and a spot diameter of 140 μm. Each array consisted of six identical segments. All scFvs were printed in six spot replicates with one replicate in each segment. The on-chip scFv concentrations ranged from 40 to 193 μg/mL depending on binding properties of the individual clone. In addition, several spot positions were left empty as “negative reference spot control”, and one reference marker (biotinylated BSA) were used to guide the automatic grid alignment process, but not for any quantitative purposes. Printed microarray slides were stored in dark in a climate-controlled room, at 22° C. and 40% relative humidity, for at least 5 days before being used in microarray assays.

Immunoprecipitation Mass Spectrometry (IP-MS)

In order to verify target binding of the scFv antibodies and to identify potential interacting proteins in serum, affinity pull-down assays were conducted. Briefly, purified histidine-tagged scFvs were immobilized onto magnetic nickel beads (MagneHis Ni-particles, Promega). Four technical replicates per antibody were used and non-coated beads were included as negative controls to subtract non-specific binding to the beads. The scFv coated and control beads were incubated for 20 min with pooled normal human serum (diluted 14-fold in PBS). The beads were washed three times to attenuate non-specific binding.

Captured proteins were eluted from the beads and digested with trypsin over night at 37° C. Trypsin activity was quenched by addition of formic acid and digested samples were dried down in a speed-vac. Dried peptides were cleaned using UltraMicroSpin C18 columns (Nest Group) and re-suspended in 0.1% formic acid for LC-MS/MS analysis.

The peptide pool was analyzed using a Q-Exactive HF-X mass spectrometer coupled to a nanoLC system (Thermo Fisher Scientific). The Xcalibur software v. 3.0 was used to control and acquire the data from Q-Exactive HF-X. Peptide identification of obtained data was carried out using Mascot Server (v. 2.4.1) for searches against the THISP2 database (http://www.peptideatlas.org/thisp/), and quantification of peptides at the MS1 level using Dinosaur, through the Proteios Software Environment (http://www.proteios.org).

Focusing on the resulting biomarker signature, the affinity pull-down assays identified potential secondary targets for three scFvs (Table 3). Interacting proteins can either be the result of off-target binding by the antibody (direct co-enrichment) or due to interaction of the intended target with another protein (indirect co-enrichment) (Fredolini et al., 2019). Hence, the von Willebrand factor (VWF) was captured by the scFv directed against osteoprotegerin (OPG). It has been reported that OPG is able to form a complex with VWF (Shahbazi et al., 2007), which points to the indirect route of co-enrichment. Moreover, the scFv antibody initially directed against HADH2 was shown to bind gelsolin (GSN) and the scFv antibody directed against MUC16 (CA125) to tentatively bind the complement lectin pathway components, ficolin-2 (L-ficolin; FCN2) and mannan-binding lectin serine protease 2 (MASP2).

Microarray Assay

In general, 12 patient samples were analyzed on each microarray slide. The positioning of the samples was randomized but the distribution of PDAC and control samples was approximately the same across slides and runs. Apart from disease status, samples were also stratified based on sub-group (e.g. stage or w/wo diabetes), gender, age group, cohort and collection year. Two arrays on each slide were used for a control sample (pool of serum from healthy individuals) for slide normalization purposes. On each assay run, two microarray slides were dedicated for quality control (QC) purposes.

Each microarray slide was mounted in a hybridization gasket (Schott) and blocked with 1% w/v milk, 1% v/v Tween-20 in sterile PBS (MT-PBS) at RT for 2 hours with constant agitation. Meanwhile, aliquots of labelled serum samples were thawed on ice and subsequently diluted 1:50 in MT-PBS. The slides were washed four times with 0.05% Tween-20 in sterile PBS (T-PBS) followed by addition of diluted serum samples to the wells of the gasket.

Samples were incubated on the slides at RT for 2 hours with constant agitation. Next, the slides were washed four times with T-PBS, incubated with 1 μg/ml Streptavidin Alexa-647 (Life Technologies) in MT-PBS at RT for 1 hour with constant agitation, and again washed four times with T-PBS. Finally, the slides were dismounted from the hybridization gaskets, immersed in dH₂O and dried under a stream of N2. The slides were immediately scanned with a microarray fluorescence scanner, InnoScan 710 AL (Innopsys), at a laser excitation wavelength of 635 nm using a resolution of 10 μm/pixel.

Data Acquisition, Quality Control, and Pre-Processing

Grid alignment and spot signal quantitation were performed using our in-house, proprietary software platform, Immunovia Evaluation System (IES). Spot signal intensities were quantified using the fixed circle method.

Each data point represented the median, background-subtracted, signal of six replicate spots unless any replicate(s) did not pass the applied z-filtering acceptance criteria and were flagged as position failures. In this case, the worst performing replicate(s) was eliminated and the median value of the remaining replicates was used instead. Log2 values of signal intensities were used, and no imputation was applied on the data.

In the next step, data was normalized. Using our normalization approach, slide-to-slide variation was handled by calculating normalization factors for each microarray slide, per each antibody. This factor was based on the signals for the technical replicate (serum pool from healthy individuals) applied to the same positions on each slide across the study.

All computations were done with R environment (R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria). Following data normalization, outlier detection/removal was performed through the arrayQualityMetrics R package (Kauffmann et al., 2009; Kauffmann and Huber, 2010), which computes the Kolmogorov-Smirnov statistic between each array's intensity distribution and the distribution of the pooled data. Finally, we added ELISA data for CA19-9 (also log 2 transformed) into the dataset prior to data analysis.

All-in-all, samples from 36 donors were removed during quality control and pre-processing, and the final data set contained data from 1113 individuals.

Data Analysis

Of note, the 38 scFv-based microarray used in this study was derived from preceding discovery runs comprising 396 scFv antibodies (Table 2). To this end, data was partitioned into a training (50%) and a test set (50%). A feature selection step was then conducted on the training set and the predictive performance of the selected biomarkers was evaluated using only the test set (data not shown).

By this means, we identified 38 scFv antibodies (directed against 33 unique proteins) as candidate probes for detection of PDAC. In the current study the aim was to verify the usefulness of these scFvs by using several novel independent sample cohorts, and to attempt to condense the list of protein biomarkers further. To this end, we focused on three main criteria for assessing the candidate scFvs for the intended use. These three main categories were (i) difference of relative expression (fold change) between cases and controls (ii) prediction performance and (iii) analytical performance and robustness. This has been done on a predefined fraction of data.

Hence, we firstly assessed each scFv antibody with respect to strength and direction of fold change (FC). FC for a given antibody is the difference between the average, log 2-transformed, values of two groups (i.e. PDAC and controls). Secondly, the prediction performance of the scFv antibodies was evaluated using the variable importance returned by the Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, applying multivariate and dependent feature selection. In the third step, we assessed each scFv antibody in terms of variability (expressed as microarray inter- and intra-assay CV values) in order to select probes displaying robust analytical performance. In addition, to decipher an optimally condensed biomarker signature, scFvs showing high level of correlation to other antibodies were removed to minimize redundancy.

Subsequently, the classification performance of the resulting biomarker signature was assessed using support vector machine algorithm (SVM) and results was evaluated by generating receiver operating characteristic (ROC) curves for various group comparisons (e.g. PDAC vs. healthy controls), and the corresponding area under the curve (AUC) values were calculated. When building the classification model, data was divided into a training set including 70% of the samples and a test set including 30% of the samples. A large number of cross validations were performed and ROC AUC, sensitivity and specificity values were calculated for each potential model. The predictive performance of the model was evaluated for different subsets of the biomarkers and both with and without CA19-9 added to the signature (Table 4).

Results

Recombinant antibody microarrays are based on immobilized antibody derivatives for parallel analysis of multiple analytes in a minute amount of liquid biopsy (usually serum or plasma). The current study was based on antibody microarrays comprising 38 single-chain variable fragments (scFvs) directed against 33 different antigens (Table 1). These 38 scFvs had been selected from a repertoire of 396 scFvs (Table 2) by preceding discovery studies as described in the Method section (data not shown). As mentioned previously, all scFv antibodies included in this study are built on a scaffold that has been shown to display appropriate adsorption properties and high functional stability when kept in a dried-out state on the microarray slide (Steinhauer et al., 2002, Säll et al., 2016).

Using 1113 serum samples from several different sites in USA and Europe, this study represents to the best of our knowledge one of the largest multicenter analysis of biomarker panels for predicting pancreatic cancer that has been conducted so far. In addition, this study differs from previous affinity proteomic studies on pancreatic cancer (e.g. Wingren et al., 2012, Gerdtsson et al., 2015, Mellby et al., 2018) in two important aspects. Firstly, the study is based on a reduced, 38-plex, microarray consisting entirely of informative probes rather than a high-density microarray, and secondly, the sample set used in this study included a large number (n=488) of symptomatic controls carefully selected to represent the intended clinical use population of the test.

The generated microarray data allowed us to condense the biomarker signature down to eight unique proteins (Table 3). As described in detail in the Method section, this was done by assessing FC values, prediction performance and analytical variability of individual scFv antibodies. FIG. 1 shows a volcano plot of the entire data set (PDAC vs all controls) for the corresponding scFv antibody clones.

The classification performance of the derived 8-plex signature was evaluated by generating ROC curves for various group comparisons and the corresponding AUC values were calculated. FIG. 2-4 shows ROC curves for classifications of PDAC vs. healthy controls, PDAC vs. symptomatic controls and PDAC vs all controls, respectively. When combining the 8-plex microarray signature with data from the CA19-9 assay, AUC values of 0.96, 0.93 and 0.94 were achieved.

Most importantly, an ROC-AUC value of 0.95 was calculated for the discrimination of samples from patients with early PDAC (stage I/II) versus all controls (healthy and symptomatic) when using the combination of the 8-plex signature with data from the CA19-9 assay (FIG. 5 ).

Since differential diagnosis of early PDAC vs. pancreatitis can be particularly challenging (Klöppel and Adsay, 2009), we proceeded to test the classification ability of our biomarker signature with regard to this subgroup analysis. Even though the number of chronic pancreatitis samples was limited in the current study (n=56), our data suggest that chronic pancreatitis can be discriminated from early stage I/II PDAC with a ROC-AUC of 0.92 (FIG. 6 ).

Discussion

In this study, we have shown that a recombinant antibody microarray-based biomarker signature of eight proteins can significantly improve upon the predictive performance of serum CA19-9 analysis as well as enhance the discrimination between PDAC and various benign diseases overlapping in symptoms with early PDAC. Importantly, we could demonstrate that by combining proteomic multiplex analysis with a CA19-9 ELISA, early stage I/II PDAC patient samples could be separated from all controls with a ROC-AUC of 0.95. It should be stressed that these results have been achieved using collected serum samples (n=1113) from seven different pancreatic disease reference sites in USA and Europe to avoid a number of potential biases from sample collection, processing, and storage that can occur when using archival specimens (Zhang and Chan, 2005).

The sialyl Lewis A tetrasaccharide epitope (carbohydrate antigen 19-9; CA19-9) is the most widely clinically used tumor marker for pancreatic cancer, but alone it lacks the necessary sensitivity and specificity for diagnosis and is not recommended for screening purposes. When using an assay cut-off value of 37 U/mL, CA19-9 has been reported to have a median sensitivity of 81% and specificity of 90% for pancreatic cancer, whereas increasing the threshold to 100 U/mL improves specificity to 98% but reduces sensitivity to 68% (Steinberg, 1990). However, later it has been pointed out that the cited analysis mainly involved healthy subjects as controls and therefore does not reflect the clinical population of concern (Poruk et al., 2013). When the accuracy of CA19-9 was re-evaluated in a clinically more relevant context, the apparent functional sensitivity and specificity for CA19-9 was approximately 80% each at the conventional clinical cut-off of 37 U/ml.

By combining CA19-9 measurements with multiparametric microarray analysis, we could generate a SVM model algorithm that yields a specificity of 95% and a sensitivity of 83% discriminating PDAC samples from clinically relevant controls. Since CA19-9 is increased in a number of benign diseases (including pancreatitis, cirrhosis, cholangitis and obstructive jaundice), the use of relevant controls is instrumental for evaluating the clinical usefulness of potential PDAC biomarkers. In this context, we could demonstrate that stage I and II PDAC samples could be discriminated from chronic pancreatitis with a ROC-AUC of 0.92 (FIG. 6 ). Moreover, we observed that the composite signature increased the ROC-AUC value by approximately 5% when separating PDAC from symptomatic controls, as compared to using CA19-9 alone.

We see a great applicability of the SVM model algorithm as an aid for early detection of developing pancreatic cancers in patient groups with higher prevalence of the disease. These high-risk groups could include (i) individuals with a family history of pancreatic cancer or certain hereditary predispositions (Singhi et al., 2019); (ii) late onset diabetic patients over the age of 50 years, who have up to eight times increased risk for acquiring PDAC within the first three years of diabetes (Chari et al., 2005, Batabyal et al., 2014), and (iii) patients with vague but alarming symptoms potentially suggestive of pancreatic cancer, such as back and abdominal pain, jaundice and weight loss (Keane et al., 2014i).

Taken together, a diagnostic test for PDAC in high-risk patient groups holds the potential for earlier detection of malignancy, which significantly could contribute to increased tumor resectability and thereby an overall reduction in cancer-specific mortality (Pelaez-Luna et al., 2007).

As previously mentioned, the microarray-based biomarker signature yielding the highest specificity/sensitivity combination is directed against eight different proteins (Table 3). Three of these proteins, i.e. GSN, IGFBP3 and OPG, have previously been reported to be implicated in pancreatic cancer (Ni et al., 2008, Yoneyama et al., 2016, Shi et al., 2014). We observed an increased expression level of OPG, but reduced levels of GSN and IGFBP3, in PDAC serum samples (FIG. 1 ). This appears to be in accordance with the general literature on the topic. However, different antibodies might recognize distinct forms of circulating analytes (i.e. free, complex and fragmented), so conflicting results would not necessarily be unexpected.

The serum levels of MUC16 (CA125) is a classical tumor marker used in ovarian cancer for diagnosis, detection of early recurrence, and for monitoring of the therapeutic effects of treatments (Felder et al., 2014). Reports about the involvement of MUC16 in pancreatic cancer are much scarcer. However, MUC16 seems to have a diagnostic value in cases of pancreatic cancer where CA19-9 is not elevated, particularly in those patients who are negative for the Lewis antigen (Liu et al., 2016).

The presence of several complement proteins (e.g. C4, C5 and Factor B) in the signature points to an important role of the complement system in pancreatic cancer. Cancer related information in the literature of complement is relatively sparse, but the involvement of complement in pancreatic diseases has been recognized (Bettac et al., 2017). Although inherently complex, complement activation is generally considered protective against cancer. The possible involvement FCN2 and MASP2 in the signature suggest a novel role for the lectin induced pathway in PDAC. FCN2 has previously been shown to directly associate with MASP2 to trigger the lectin complement pathway (Lacroix et al., 2009).

Finally, we observed increased serum levels of VWF and Cystatin C in PDAC samples (FIG. 1 ). VWF plays a major role in blood coagulation, and it seems plausible that it could be involved in the issue of venous thromboembolism (VTE) that affects a large percentage of PDAC patients (Faille et al., 2018). Cystatin C is an inhibitor of the cysteine proteinase, cathepsin B, and is an established clinical marker for estimating glomerular filtration rate (Ferguson et al., 2015). However, Cystatin C has previously also been implicated in cancer and was identified as a candidate marker of PDAC in a global proteome analysis of pancreatic juice samples (Kos et al., 2000, Gronborg et al., 2004). However, there has not previously been any suggestion to use the measurement of such biomarkers in combination to produce a signature for diagnosis of pancreatic cancer.

In summary, our results demonstrate that combining data from an 8-plex recombinant antibody microarray test with that of a CA19-9 assay can detect samples derived from patients with stage I and II PDAC with high predictive performance. Hence, this clearly points to the possibility of diagnosing pancreatic cancer at an earlier stage, using a serum biomarker signature.

We envision that this test regimen can be made readily available for clinicians in primary and secondary care for surveillance of high-risk populations and for diagnosis of patients with symptoms suggestive of early pancreatic cancer.

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TABLE 1 38-plex microarray scFvs scFv clone Antigen/Biomarker Exemplary SEQ ID NO. TGF-β1 (1) TGF-β1 1 IFN-γ (2) IFN-γ 2 PSA PSA 3 C3 (1) C3 4 C3 (2) C3 5 C5 (2) C5 6 C1 inh. (1) C1 esterase inhibitor 7 Properdin Properdin 8 VEGF (3) VEGF 9 CD40 (1) CD40 10 Cystatin C (1) Cystatin C 11 Apo-A1 (3) Apolipoprotein A1 12 Factor B (2) Factor B 13 Factor B (4) Factor B 14 C4 (3) C4 15 C3 (3) C3 16 MYOM2 (1) MYOM2 17 CHX10 (3) CHX10 18 BTK (3) BTK 19 HADH2 (3) HADH2/GSN 20 MATK (1) MATK 21 TNFRSF3 (2) TNFRSF3 22 UBC9 (3) UBC9 23 UBP7 (1) UBP7 24 UBP7 (2) UBP7 25 APLF (2) APLF 26 MARK1-1 (1) MARK1-1 27 PTPPRN2 (1) PTPRN2 28 CEACAM5 (1) CEACAM-5 29 MUC16 (1) MUC16 30 MMP9 (2) MMP9 31 IGFBP3 (2) IGFBP3 32 IL-2ra (3) IL-2Ra 33 AGR2 (1) AGR2 34 IGFBP2 (1) IGFBP2 35 OPG (2) OPG 36 GDF-15 (1) GDF-15 37 CEACAM5 (3) CEACAM-5 38

TABLE 2 Discovery phase scFv specificities; n = 396 No. of Antigen/Biomarker Full name scFvs AGAP-2 Arf-GAP with GTPase, ANK repeat and PH 3 domain-containing protein 2 AGR2 Anterior gradient protein 2 homolog 1 AGR3 Anterior gradient protein 3 1 AKT3 RAC-gamma serine/threonine-protein kinase 2 Angiomotin Angiomotin 2 ANM5 Protein arginine N-methyltransferase 5 2 APLF Aprataxin and PNK-like factor 2 APOA1 Apolipoprotein A1 3 APOA4 Apolipoprotein A4 3 ARHGC Rho guanine nucleotide exchange factor 12 1 ATP5B ATP synthase subunit beta, mitochondrial 2 β-galactosidase Beta-galactosidase 1 BIRC2 Baculoviral IAP repeat-containing protein 2 2 BTK Tyrosine-protein kinase BTK 4 C1 esterase inhibitor Plasma protease C1 inhibitor 3 C1q Complement C1q 1 C1s Complement C1s 1 C3 Complement C3 4 C4 Complement C4 4 C5 Complement C5 3 CD40 CD40 protein 4 CD40L CD40 ligand 1 CDK2 Cyclin-dependent kinase 2 2 CEACAM-1 Carcinoembryonic antigen-related cell adhesion molecule 1 2 CEACAM-5 Carcinoembryonic antigen-related cell adhesion molecule 5 3 CHEK2 Serine/threonine-protein kinase Chk2 2 CHP1 Calcineurin B homologous protein 1 2 CHX10 Visual system homeobox 2 3 CSNK1E Casein kinase I isoform epsilon 2 CTLA-4 Cytotoxic T-lymphocyte protein 4 2 Cystatin B Cystatin B 1 Cystatin C Cystatin-C 4 DCNL1 DCN1-like protein 1 2 DKK-1 Dickkopf-related protein 1 2 DLG1 Disks large homolog 1 2 DLG2 Disks large homolog 2 2 DLG4 Disks large homolog 4 2 DPOLM DNA-directed DNA/RNA polymerase mu 2 DUSP7 Dual specificity protein phosphatase 7 2 DUSP9 Dual specificity protein phosphatase 9 1 EGFR Epidermal growth factor receptor 2 Eotaxin Eotaxin 3 Factor B Complement factor B 3 FASN Fatty acid synthase 4 FER Tyrosine-protein kinase Fer 2 FGF2 Fibroblast growth factor 2 2 GAK GAK protein 3 GDF-15 Growth/differentiation factor 15 1 GEM GTP-binding protein GEM 2 GLP-1 Glucagon-like peptide-1 1 GLP-1R Glucagon-like peptide 1 receptor 1 GM-CSF Granulocyte-macrophage colony-stimulating 4 factor GNAI3 Guanine nucleotide-binding protein G(k) subunit alpha 2 GORS2 Golgi reassembly-stacking protein 2 2 GPRK5 G protein-coupled receptor kinase 5 1 GRIP2 Glutamate receptor-interacting protein 2 2 HADH2 HADH2 protein 4 Her2/ErbB2 Receptor tyrosine-protein kinase erbB-2 4 HLA-DR/DP HLA-DR/DP 1 ICAM-1 Intercellular adhesion molecule 1 1 IFN-γ Interferon gamma 3 IGFBP2 Insulin-like growth factor-binding protein 2 1 IGFBP3 Insulin-like growth factor-binding protein 3 1 IgM IgM 4 IL-10 Interleukin-10 2 IL-11 Interleukin-11 3 IL-12 Interleukin-12 4 IL-13 Interleukin-13 1 IL-16 Interleukin-16 2 IL-18 Interleukin-18 3 IL-1-ra Interleukin-1 receptor antagonist protein 3 IL-1α Interleukin-1 alpha 3 IL-1β Interleukin-1 beta 2 IL-2 Interleukin-2 1 IL-2-ra Interleukin-2 receptor subunit alpha 1 IL-3 Interleukin-3 3 IL-4 Interleukin-4 3 IL-5 Interleukin-5 1 IL-6 Interleukin-6 4 IL-7 Interleukin-7 1 IL-8 Interleukin-8 3 IL-9 Interleukin-9 2 INADL InaD-like protein 2 Integrin α-11 Integrin alpha-11 1 ITCH E3 ubiquitin-protein ligase Itchy homolog 2 JAK3 Tyrosine-protein kinase JAK3 1 KCC2B Calcium/calmodulin-dependent protein kinase 2 type II subunit beta KCC4 Calcium/calmodulin-dependent protein kinase 2 type IV Keratin 19 Keratin, type I cytoskeletal 19 3 KKCC1 Calcium/calmodulin-dependent protein kinase 1 2 KRASB GTPase KRas 1 KSYK Tyrosine-protein kinase SYK 1 LDL Apolipoprotein B-100 2 Leptin Leptin 1 Lewis x Lewis x 2 Lewis y Lewis y 1 LIF Leukemia inhibitory factor 2 LIN7A Protein lin-7 homolog A 2 LUM Lumican 1 MAGI1 Membrane-associated guanylate kinase, 2 WW and PDZ domain-containing protein 1 MAP2K2 Dual specificity mitogen-activated protein kinase 2 2 MAP2K6 Dual specificity mitogen-activated protein kinase 6 4 MAPK9 Mitogen-activated protein kinase 9 2 MARK1 Serine/threonine-protein kinase MARK1 2 MARK2 Serine/threonine-protein kinase MARK2 2 MATK Megakaryocyte-associated tyrosine-protein 2 kinase MCP-1 C-C motif chemokine 2 3 MCP-3 C-C motif chemokine 7 2 MCP-4 C-C motif chemokine 13 1 MD2L1 Mitotic spindle assembly checkpoint protein 2 MAD2A MK01 Mitogen-activated protein kinase 1 4 MK08 Mitogen-activated protein kinase 8 3 MMP3 Matrix metalloproteinase-3 2 MMP9 Matrix metalloproteinase-9 2 MSLN Mesothelin 2 MUC1 Mucin-1 4 MUC16 Mucin-16 2 MYOM2 Myomesin-2 2 NDC80 Kinetochore protein NDC80 homolog 2 NOS1 Nitric oxide synthase, brain 2 OPG Osteoprotegerin 1 OSBPL3 Oxysterol-binding protein-related protein 3 2 OSTP Osteopontin 3 OTU6B OTU domain-containing protein 6B 2 OTUB1 Ubiquitin thioesterase OTUB1 2 OTUB2 Ubiquitin thioesterase OTUB2 2 OX40 Tumor necrosis factor ligand superfamily member 4 2 P85A Phosphatidylinositol 3-kinase regulatory subunit 3 alpha PAK4 Serine/threonine-protein kinase PAK 4 2 PAK5 Serine/threonine-protein kinase PAK 5 2 PARP1 Poly [ADP-ribose] polymerase 1 1 PARP6B Partitioning defective 6 homolog beta 1 PD-1 Programmed cell death protein 1 3 PD-L1 Programmed cell death 1 ligand 1 2 PD-L2 Programmed cell death 1 ligand 2 2 PGAM5 Serine/threonine-protein phosphatase PGAM5, 2 mitochondrial PRD14 PR domain zinc finger protein 14 3 PRDM8 PR domain zinc finger protein 8 2 PRKCZ Protein kinase C zeta type 2 PRKG2 cGMP-dependent protein kinase 2 2 Procathepsin W Cathepsin W 1 Prolactin Prolactin 2 Properdin Properdin 1 PSA Prostate-specific antigen 1 PTK6 Protein-tyrosine kinase 6 1 PTN13 Tyrosine-protein phosphatase non-receptor type 13 2 PTPN1 Tyrosine-protein phosphatase non-receptor type 1 3 PTPRD Receptor-type tyrosine-protein phosphatase delta 2 PTPRJ Receptor-type tyrosine-protein phosphatase eta 4 PTPRK Receptor-type tyrosine-protein phosphatase 4 kappa PTPRN2 Receptor-type tyrosine-protein phosphatase N2 2 PTPRO Receptor-type tyrosine-protein phosphatase O 3 PTPRT Receptor-type tyrosine-protein phosphatase T 2 RANTES C-C motif chemokine 5 2 RPS6KA2 Ribosomal protein S6 kinase alpha-2 3 SHC1 SHC-transforming protein 1 2 Sialyl Lewis a Sialyl Lewis a 1 Sialyl Lewis x Sialyl Lewis x 1 SNTA1 Alpha-1-syntrophin 2 Sox11a Transcription factor SOX-11 1 SPDLY Protein Spindly 2 STAP1 Signal-transducing adaptor protein 1 2 STAP2 Signal-transducing adaptor protein 2 4 STAT1 Signal transducer and activator of transcription 1-alpha/beta 2 TBC1D9 TBC1 domain family member 9 2 TENS4 Tensin-4 1 TFPI Tissue factor pathway inhibitor 2 TGF-β1 Transforming growth factor beta-1 3 TNFRSF14 Tumor necrosis factor receptor superfamily 2 member 14 TNFRSF3 Tumor necrosis factor receptor superfamily 3 member 3 TNF-α Tumor necrosis factor 3 TNF-β Lymphotoxin-alpha 4 TOPB1 DNA topoisomerase 2-binding protein 1 2 TTR Transthyretin 2 TXLNA Alpha-taxilin 2 UBC9 SUMO-conjugating enzyme UBC9 3 UBE2C Ubiquitin-conjugating enzyme E2 C 2 UBP7 Ubiquitin carboxyl-terminal hydrolase 7 3 UCHL5 Ubiquitin carboxyl-terminal hydrolase isozyme L5 1 UPAR Urokinase plasminogen activator surface receptor 2 UPF3B Regulator of nonsense transcripts 3B 2 VEGF Vascular endothelial growth factor 3 WFDC2 WAP four-disulfide core domain protein 2 2

TABLE 3 8-plex biomarker signature Secondary scFv clone Primary target Uniprot ID target(s) Uniprot ID C4 (3) Complement C4 P0C0L4/5 N/A N/A C5 (2) Complement C5 P01031 N/A N/A Cystatin C (1) Cystatin C P01034 N/A N/A Factor B (2) Complement Factor B P00751 N/A N/A HADH2 (3) GSN P06396 HADH2 Q99714 IGFBP3 (2) IGFBP-3 P17936 N/A N/A MUC16 (1) Mucin-16 Q8WXI7 FCN2, MASP2 Q15485, O00187 OPG (2) OPG O00300 VWF P04275

TABLE 4 Exemplary discriminating power of biomarkers and biomarker combinations (PDAC vs All controls). ROC-AUC Biomarker signature 0.929 CA19-9 + GSN¹ + OPG² 0.931 CA19-9 + GSN¹ + OPG² + Factor B 0.933 CA19-9 + GSN¹ + OPG² + IGFBP3 0.927 CA19-9 + OPG² + Factor B + IGFBP3 0.923 CA19-9 + OPG² + IGFBP3 + MUC16³ 0.938 CA19-9 + GSN¹ + OPG² + Factor B + IGFBP3 0.939 CA19-9 + GSN¹ + OPG² + Factor B + IGFBP3 + C5 0.941 CA19-9 + GSN¹ + OPG² + Factor B + IGFBP3 + C5 + MUC16³ 0.941 CA19-9 + GSN¹ + OPG² + Factor B + IGFBP3 + C5 + MUC16³ + Cystatin C 0.940 CA19-9 + GSN¹ + OPG² + Factor B + IGFBP3 + C5 + MUC16³ + Cystatin C + C4 0.932 CA19-9 + GSN¹ + Factor B + C5 + Cystatin C + C4 0.924 CA19-9 + IGFBP3 + Factor B + C5 + Cystatin C + C4 0.919 CA19-9 + MUC16³ + Factor B + C5 + Cystatin C + C4 0.896 CA19-9 + Factor B + C5 + Cystatin C + C4 0.838 GSN¹ + OPG² 0.850 GSN¹ + OPG² + Factor B 0.861 GSN¹ + OPG² + IGFBP3 0.848 OPG² + Factor B + IGFBP3 0.845 OPG² + IGFBP3 + MUC16³ 0.873 GSN¹ + OPG² + Factor B + IGFBP3 0.878 GSN¹ + OPG² + Factor B + IGFBP3 + C5 0.885 GSN¹ + OPG² + Factor B + IGFBP3 + C5 + MUC16³ 0.884 GSN¹ + OPG² + Factor B + IGFBP3 + C5 + MUC16³ + Cystatin C 0.884 GSN¹ + OPG² + Factor B + IGFBP3 + C5 + MUC16³ + Cystatin C + C4 0.854 GSN¹ + Factor B + C5 + Cystatin C + C4 0.833 IGFBP3 + Factor B + C5 + Cystatin C + C4 0.809 MUC16³ + Factor B + C5 + Cystatin C + C4 0.645 Factor B + C5 + Cystatin C + C4 ¹And/or HADH2, ²And/or VWF, ³And/or MASP2 and/or FCN2

TABLE 5 Amino acid sequences of the scFv antibodies directed against the biomarkers in Table 3 *The molecular architecture of the scFv constructs is described in Söderlind et al., 2000, and Säll et al., 2016. ScFv clone Antigen(s) Amino acid sequence C4 (3) Complement C4 EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAISGSGGSTYYADSVKGRFTIS RDNSKNTLYLQMNSLRAEDTAVYYCARGWSTSSFDYWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPS ASGTPGQRVTISCSGSSSNIGNHYVSWYQQLPGTATKLLIYYDDLLPSGVPDRFSGSKSGTSASLAISGLR SEDEADYYCAAWDDRSGQVLFGGGTKLTVLGDYKDHDGDYKDHDIDYKDDDDKAAAHHHHHH* [SEQ ID NO: 15] C5 (2) Complement C5 EVQLLESGGGLVQPGGSLRLSCAASGFTFSAYSMNWVRQAPGKGLEWVSGVSWNGSRTHYADSVKGRFTIS RDNSKNTLYLQMNSLRAEDTAVYYCARENSGFFDYWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSA SGTPGQRVTISCTGSSSNIGSNTVNWYQQLPGTAPKLLIYGNSNRPSGVPDRFSGSKSGTSASLTISGLRS EDEADYYCAAWDDSLSGWVFGGGTKLTVLGEQKLISEEDLSGSAAAHHHHHH* [SEQ ID NO: 6] Cystatin C Cystatin C EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMNWVRQAPGKGLEWVGLISYDGRTTYYADSVKGRSTIS (1) RDNSKNTLYLQMNSLRAEDTAVYYCATTTGTTLDYWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSA SGTPGQRVTISCTGSSSNIGAGYDVHWYQQLPGTAPKLLIYGNTNRPSGVPDRFSGSKSGTSASLAISGLR SEDEADYYCAAWDDSLYGWVFGGGTKLTVLGDYKDHDGDYKDHDIDYKDDDDKAAAHHHHHH* [SEQ ID NO: 11] Factor B Complement EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYSMNWVRQAPGKGLEWVAVISYDGRFIYYSDSVKGRFTIS (2) Factor B RDNSKNTLYLQMNSLRAEDTAVYYCARSYGGNLAMDVWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPP SASGTPGQRVTISCTGSSSNIGAGYDVHWYQQLPGTAPKLLIYDNNKRPSGVPDRFSGSNSGTSASLAISG LRSEDEADYYCAAWDDRLNGRVVFGGGTKLTVLGDYKDHDGDYKDHDIDYKDDDDKAAAHHHHHH* [SEQ ID NO: 13] HADH2 HADH2 and/or EVQLLESGGGLVQPGGSLRLSCAASGFTFGSSYMSWVRQAPGKGLEWVSSISSYGYYTGYADSVKGRFTIS (3) GSN RDNSKNTLYLQMNSLRAEDTAVYYCARSYGSWYFDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPS SLSASVGDRVTITCRASQSISSYLNWYQQKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQP EDFATYYCQQGFVGPSTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKAAAHHHHHH* [SEQ ID NO: 20] IGFBP3 IGFBP3 EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAISGSGGSTYYADSVKGRFTIS (2) RDNSKNTLYLQMNSLRAEDTAVYYCARGGGIVFDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSS LSASVGDRVTITCRASQSISSYLNWYQQKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPE XFATYYCQQSYSTPYTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKAAAHHHHHH* [SEQ ID NO: 32] MUC16 MUC16 and/or EVQLLESGGGLVQPGGSLRLSCAASGFTFNNYAMNWVRQAPGKGLEWVSGIGYYGSYTSYADSVKGRFTIS (1) FCN2 and/or RDNSKNTLYLQMNSLRAEDTAVYYCARDYSFDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLS MASP2 ASVGDRVTITCRASQSISSYLNWYQQKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDF ATYYCQQGFYPYTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKAAAHHHHHH* [SEQ ID NO: 30] OPG (2) OPG and/or EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAISGSGGSTYYADSVKGRFTIS VWF RDNSKNTLYLQMNSLRAEDTAVYYCARDFYSYSGFDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSP SSLSASVGDRVTITCRASQSISSYLNWYQQKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQ PEDFATYYCQQYGYYPLTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKAAAHHHHHH* [SEQ ID NO: 36]

TABLE 6 Amino acid sequences of the scFv antibodies listed in Table 1 *The molecular architecture of the scFv constructs is described in Söderlind et al., 2000, and Säll et al., 2016. ScFv clone Amino acid sequence TGF-β1 EVQLLESGGGLVQPGGSLRLSCAASGFTFSTYAMSWVRQAPGKGLEWVAVVSIDGGTTYYGDPVKGRFTISRDNSKNTLYLQMNSLRA (1) EDTAVYYCTRGPTLTYYFDYWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSSSNIGSNTVNWYQQLPG TAPKLLIYSNNQRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCQSYDSSLSGWVFGGGTKLTVLGEQKLISEEDLSGSAAAHHH HHH* [SEQ ID NO: 1] IFN-γ EVQLLESGGGLVQPGGSLRLSCAASGFTFSRHGFHWVRQGPGKGLEWVSGVSWNGSRTHYADSVKGRFTISRDNSKNTLYLQMNSLRA (2) EDTAVYYCARGNWYRAFDIWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGGSSHIGRNFISWYQQLPGT APKLLIYAGNSRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCAAWDDSLSGVVFGGGTKLTVLGEQKLISEEDLSGSAAAHHHH HH* [SEQ ID NO: 2] PSA EVQLLESGGGLVQPGGSLRLSCAASGFTFRSYEMNWVRQAPGKGLEWVAVIGGNGVDTDYADSVKGRFTISRDNSKNTLYLQMNSLRA EDTAVYYCVREEVDFWSGYYSYGMDVWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSSSNIGDNFVSW YQQLPGTAPKLLIYRTNGRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCATWDDNLNGRVVFGGGTKLTVLGDYKDDDDKAAAH HHHHH* [SEQ ID NO: 3] C3 EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSSVTGSGGGTYYADSVEGRFTISRDNSKNTLYLQMNSLRA (1) EDTAVYYCARYRWFGNDAFDIWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSASNLGMHFVSWYQQLP GTAPKLLIYGNSNRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCAAWDDTLNIWVFGGGTKLTVLGEQKLISEEDLSGSAAAHH HHHH* [SEQ ID NO: 4] C3 EVQLLESGGGLVQPGGSLRLSCAASGFTFSTYRMIWVRQAPGKGLEWVSSISGSNTYIHYADSVRGRFTISRDNSKNTLYLQMNSLRA (2) EDTAVYYCARDRHPLLPSGMDVWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSSSNIGKHPVNWYQQL PGTAPKLLIYRNDQRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCQSYDSSLSGSWVFGGGTKLTVLGEQKLISEEDLSGSAAA HHHHHH* [SEQ ID NO: 5] C5 EVQLLESGGGLVQPGGSLRLSCAASGFTFSAYSMNWVRQAPGKGLEWVSGVSWNGSRTHYADSVKGRFTISRDNSKNTLYLQMNSLRA (2) EDTAVYYCARENSGFFDYWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCTGSSSNIGSNTVNWYQQLPGTA PKLLIYGNSNRPSGVPDRFSGSKSGTSASLTISGLRSEDEADYYCAAWDDSLSGWVFGGGTKLTVLGEQKLISEEDLSGSAAAHHHHH H* [SEQ ID NO: 6] C1 EVQLLESGGGLVQPGGSLRLSCAASGFTFSDYYMSWIRQAPGKGLEWVSGISRGGEYTFYVDSVKGRFTISRDNSKNTLYLQMNSLRA inh. EDTAVYYCARDPGGLDAFDIWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCTGSSSNIGARYDVQWYQQLP 1 GTAPKLLIYGNSNRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCASWDDSLSGPVFGGGTKLTVLGEQKLISEEDLSGSAAAHH HHHH* [SEQ ID NO: 7] Proper- EVQLLESGGGLVQPGGSLRLSCAASGFTFSSNYMSWVRQAPGKGLEWVSAISGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRA din EDTAVYYCAKGGSGWYDYFDYWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCTGSSSNIGAGYDVHWYQQL PGTAPKLLIYRNNQRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCAAWDDGLNSPVFGGGTKLTVLGEQKLISEEDLSGSAAAH HHHHH* [SEQ ID NO: 8] VEGF EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYEMNWVRQAPGKGLEWVSGISGSGGFTYYADSVKGRFTISRDNSKNTLYLQMNSLRA (3) EDTAMYYCAREGYQDAFDIWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCTGSSSNIGAGYDVHWYQQLPG TAPKLLIYSNNQRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCAAWDDSLSGPPWVFGGGTKLTVLGEQKLISEEDLSGSAAAH HHHHH* [SEQ ID NO: 9] CD40 EVQLLESGGGLVQPGGSLRLSCAASGFTFSDYYMSWVRQAPGKGLEWVSGISGNGGYTYFADSVKDRFTISRDNSKNTLYLQMNSLRA (1) EDTAVYYCARAPVDYSNPSGMDVWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSRSNIGLNTVNWYQQ LPGTAPKLLIYGNSNRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCAGWDDSLSGWVFGGGTKLTVLGDYKDDDDKAAAHHHHH H* [SEQ ID NO: 10] Cysta- EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMNWVRQAPGKGLEWVGLISYDGRTTYYADSVKGRSTISRDNSKNTLYLQMNSLRA tin EDTAVYYCATTTGTTLDYWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCTGSSSNIGAGYDVHWYQQLPGT C APKLLIYGNTNRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCAAWDDSLYGWVFGGGTKLTVLGDYKDHDGDYKDHDIDYKDDD (1) DKAAAHHHHHH* [SEQ ID NO: 11] Apo-A EVQLLESGGGLVQPGGSLRLSCAASGFTFRDYYMSWIRQAPGKGLEWVAVTSYDGSKKYYADSVKGRFTISRDNSKNTLYLQMNSLRA (3) EDTAVYYCAKDYADDSIAAPAFDIWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCTGSSSNIGAGYDVHWY QQLPGTAPKLLIYGNSNRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCQSYDSSLSVVFGGGTKLTVLGDYKDHDGDYKDHDID YKDDDDKAAAHHHHHH* [SEQ ID NO: 12] Factor EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYSMNWVRQAPGKGLEWVAVISYDGRFIYYSDSVKGRFTISRDNSKNTLYLQMNSLRA B EDTAVYYCARSYGGNLAMDVWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCTGSSSNIGAGYDVHWYQQLP (2) GTAPKLLIYDNNKRPSGVPDRFSGSNSGTSASLAISGLRSEDEADYYCAAWDDRLNGRVVFGGGTKLTVLGDYKDHDGDYKDHDIDYK DDDDKAAAHHHHHH* [SEQ ID NO: 13] Factor EVQLLESGGGLVQPGGSLRLSCAASGFTFSKHSMNWVRQAPGKGLEWVATVSYDGNYKYYADSVKGRFTISRDNSKNTLYLQMNSLRA B EDTAVYYCAREGYYYYGMDVWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCTGSSSNIGNNAVNWYQQLPG (4) TAPKLLIYNNNQRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCQPYDDSLSSVVFGGGTKLTVLGDYKDHDGDYKDHDIDYKDD DDKAAAHHHHHH* [SEQ ID NO: 14] C4 EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAISGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRA (3) EDTAVYYCARGWSTSSFDYWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSSSNIGNHYVSWYQQLPGT ATKLLIYYDDLLPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCAAWDDRSGQVLFGGGTKLTVLGDYKDHDGDYKDHDIDYKDDD DKAAAHHHHHH* SEQ ID NO: 15] C3 EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMNWVRQAPGKGLEWVANINQDGSTKFYVDSVKGRFTISRDNSKNTLYLQMNSLRA (3) EDTAVYYCARDTGGNYLGGYYYYGMDVWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSSSNIGSNYVY WYQQLPGTAPKLLIYRNDQRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCSSYAGNNNLVFGGGTKLTVLGDYKDHDGDYKDHD IDYKDDDDKAAAHHHHHH* [SEQ ID NO: 16] MYOM2 EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSGISGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRA (1) EDTAVYYCARGVVAGSWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSSSNIGNNAVNWYQQLPGTAPK LLIYDNNKRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCAAWGXQPEWLGVRRRNQADSPRXLQRPXRXLXRSXHRLQDDDDKA AAHHHHHH [SEQ ID NO: 17] CHX10 EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVAVISYDGSNKYYADSVKGRFTISRDNSKNTLYLQMNSLRA (3) EDTAVYYCARNYGDSINWFDPWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSSSNIRSNTVNWYQQLP GTAPKLLIYGNSNRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCAAWDDSLNGWVFGGGTKLTVLGDYKDHDGDYKDHDIDYKD DDDKAAAHHHHHH* [SEQ ID NO: 18] BTK EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAISGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRA (3) EDTAVYYCARGYYGMDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGKAPK LLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQTWYLPTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKAA AHHHHHH* [SEQ ID NO: 19] HADH2 EVQLLESGGGLVQPGGSLRLSCAASGFTFGSSYMSWVRQAPGKGLEWVSSISSYGYYTGYADSVKGRFTISRDNSKNTLYLQMNSLRA (3) EDTAVYYCARSYGSWYFDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGKA PKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGFVGPSTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDD KAAAHHHHHH* [SEQ ID NO: 20] MATK EVQLLESGGGLVQPGGSLRLSCAASGFTFGSSYMGWVRQAPGKGLEWVSGIGGYGYYTGYADSVKGRFTISRDNSKNTLYLQMNSLRA (1) EDTAVYYCARYDWGHSPGSWYYGSFDYWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRVTISCSGSSSNIGSSYVY WYQQLPGTAPKLLIYSNNQRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCAAWAGAYHSHVVFGGGTKLTVLGDYKDHDGDYKD HDIDYKDDDDKAAAHHHHHH* [SEQ ID NO: 21] TNFRSF3 EVQLLESGGGLVQPGGSLRLSCAASGFTFGSSYMYWVRQAPGKGLEWVSSIYGSSSSTSYADSVKGRFTISRDNSKNTLYLQMNSLRA (2) EDTAVYYCARGYYWDYMDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGKA PKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQAWDLPTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDK AAAHHHHHH* [SEQ ID NO: 22] UBC9 EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSSIYGSSSYTYYADSVKGRFTISRDNSKNTLYLQMNSLRA (3) EDTAVYYCARSASWGGYFDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGK APKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSGVSPYTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDD DKAAAHHHHHH* [SEQ ID NO: 23] UBP7 EVQLLESGGGLVQPGGSLRLSCAASGFTFGSSSMYWVRQAPGKGLEWVSSISYYGYSTYYADSVKGRFTISRDNSKNTLYLQMNSLRA (1) EDTAVYYCARGSGIDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGKAPKL LIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQWSVYGLYTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKA AAHHHHHH* [SEQ ID NO: 24] UBP7 EVQLLESGGGLVQPGGSLRLSCAASGFTFGGSSMYWVRQAPGKGLEWVSGISYYGYSTYYADSVKGRFTISRDNSKNTLYLQMNSLRA (2) EDTAVYYCARGHSLDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGKAPKL LIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFSGYPHTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKAA AHHHHHH* [SEQ ID NO: 25] APLF EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAISGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRA (2) EDTAVYYCARGYYDMDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGKAPK LLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSYSTPYTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKA AAHHHHHH* [SEQ ID NO: 26] MARK1- EVQLLESGGGLVQPGGSLRLSCAASGFTFSGYGMGWVRQAPGKGLEWVSSISYYGGGTGYADSVKGRFTISRDNSKNTLYLQMNSLRA 1 EDTAVYYCARFDDFYASHYGIYIDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQ (1) QKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQASSLFTFGQGTKLEIKRLGDYKDHDGDYKDHDIDY KDDDDKAAAHHHHHH* [SEQ ID NO: 27] PTPPRN2 EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAISGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRA (1) EDTAVYYCARGPASSAYFDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGK APKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSYSTPYTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDD DKAAAHHHHHH* [SEQ ID NO: 28] CEACAM5 EVQLLESGGGLVQPGGSLRLSCAASGFTFGSYYMNWVRQAPGKGLEWVSGISGNGGGTNYADSVKGRFTISRDNSKNTLYLQMNSLRA (1) EDTAVYYCARAYYPAYFDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGKA PKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGYYPHTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDK AAAHHHHHH* [SEQ ID NO: 29] MUC16 EVQLLESGGGLVQPGGSLRLSCAASGFTFNNYAMNWVRQAPGKGLEWVSGIGYYGSYTSYADSVKGRFTISRDNSKNTLYLQMNSLRA (1) EDTAVYYCARDYSFDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGKAPKL LIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGFYPYTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKAAA HHHHHH* [SEQ ID NO: 30] MMP9 EVQLLESGGGLVQPGGSLRLSCAASGFTFSSSYMSWVRQAPGKGLEWVSSINSSGNGTYYADSVKGRFTISRDNSKNTLYLQMNSLRA (2) EDTAVYYCARSVYLDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGKAPKL LIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFSGYPYTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKAA AHHHHHH* [SEQ ID NO: 31] IGFBP3 EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAISGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRA (2) EDTAVYYCARGGGIVFDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGKAP KLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEXFATYYCQQSYSTPYTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDK AAAHHHHHH* [SEQ ID NO: 32] IL-2ra EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAISGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRA (3) EDTAVYYCARDAGWYGFDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGKA PKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSYSTPYTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDD KAAAHHHHHH* [SEQ ID NO: 33] AGR2 EVQLLESGGGLVQPGGSLRLSCAASGFTFGYSYMSWVRQAPGKGLEWVSGISGYYYYTYYADSVKGRFTISRDNSKNTLYLQMNSLRA (1) EDTAVYYCARVSSYHYSYIDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPG KAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEXFATYYCQQSYYWGLFTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKD DDDKAAAHHHHHH* [SEQ ID NO: 34] IGFB EVQLLESGGGLVQPGGSLRLSCAASGFTFSYYSMYWVRQAPGKGLEWVSSIGSYGYYTYYADSVKGRFTISRDNSKNTLYLQMNSLRA (1) EDTAVYYCARSWSGFHYIDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGK APKLLIYAASSLQSXVPSRFSGSGXGTDFTLTISSLQPEXFATYYCQQGYVHLLTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDD DKAAAHHHHHH* [SEQ ID NO: 35] OPG EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAISGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRA (2) EDTAVYYCARDFYSYSGFDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGK APKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYGYYPLTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDD DKAAAHHHHHH* [SEQ ID NO: 36] GDF-15 EVQLLESGGGLVQPGGSLRLSCAASGFTFSGYYMSWVRQAPGKGLEWVSSIYGYGYYTYYADSVKGRFTISRDNSKNTLYLQMNSLRA (1) EDTAVYYCARGGYFHGVSDMDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKP GKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLXISSLQPEDFATYYCXQYYNYLHTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKD DDDKAAAHHHHHH* [SEQ ID NO: 37] CEACAM5 EVQLLESGGGLVQPGGSLRLSCAASGFTFSGSYMYWVRQAPGKGLEWVSGISSSGYSTSYADSVKGRFTISRDNSKNTLYLQMNSLRA (3) EDTAVYYCARYGYYFDYWGQGTLVTVSSGGGGSGGGGSGGGGSDIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGKAPK LLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCXQFGYPPTFGQGTKLEIKRLGDYKDHDGDYKDHDIDYKDDDDKAA AHHHHHH* [SEQ ID NO: 38]

TABLE 7 SVM script A) Early PDAC vs ChrP classification ## Conduct a classification task when comparing “Early PDACs” and “Chronic Pancreatitis” using a “Frozen” SVM approach. Here, we create a single svm model on the training set (60% of the data) and then predict using the test set (40%). x = Subset_Early_CP y = Subset_Early_CP$stage_grp set.seed(2020) train_idx = createDataPartition(Subset_Early_CP$stage_grp, p = .6)$Resample1 #table(y[−train_idx]) #Create the model and then perform prediction mod = svm(x = as.data.frame(x)[train_idx,FinalSig], y = factor(y[train_idx]), kernel=“linear”,probability = TRUE) prediction = as.data.frame(attr(predict(mod, as.data.frame(x)[−train_idx,FinalSig], decision.values = TRUE,probability = TRUE), “probabilities”))$Early # Call the pdf command to start the plot pdf(file = “ROC_Early_CP_Plot_FrozenSVM.pdf”) my_roc = roc(controls = prediction[y[−train_idx] != “Early”], cases = prediction[y[−train_idx] == “Early”], plot = TRUE, print.auc = TRUE, main = paste0(“Strat 2b, 8-signature”,“: ”,“Array + CA19-9, Early PDAC vs. PC”))   points(.95,.8, pch = 20, col = “red”)  lines(x = c(1,.95,.95), y = c(.8,.8,1), col = “red”,lty = “dotted”) dev.off( ) (B) ROC curve analysis ## Details of the SVM script regarding the ROC AUC plots presented in patent application thresh_to_sen <− function(my_roc, threshold){  return(sensitivity = sum(my_roc$cases<threshold)/length(my_roc$cases)) } thresh_to_spe <− function(my_roc, threshold){  return(sum(my_roc$controls>threshold)/length(my_roc$controls)) } subROCs = function(dataset, sig, my_main, threshold_sig,threshold_sig_CA19,threshold_CA19){  dataset$ca19_9 = −dataset$ca19_9   dataset$primary_diagnosis = gsub(“ ”,“_”, dataset$primary_diagnosis)  sig_roc_res_list = list( )  for(i in 1:1000){   # Random train and test   dataset_train_idx = createDataPartition(times = 1,y =paste(dataset$site,dataset$primary_diagnosis,sep = “_”),p = .70,list = FALSE)   dataset_train_data = dataset[dataset_train_idx,] %>% arrange(dx)   dataset_test_data = dataset[−dataset_train_idx,] %>% arrange(dx)     dataset_mod_short = e1071::svm(dataset_train_data[,c(sig)], y = as.factor(dataset_train_data$dx), kernel = “linear”,probability = TRUE)   dataset_mod_short_ca19_9 = e1071::svm(dataset_train_data[,c(sig, “ca19_9”)], y = as.factor(dataset_train_data$dx), kernel = “linear”,probability = TRUE)   arr_t_pred = attr(predict(dataset_mod_short, newdata = dataset_train_data[,sig], decision.values = TRUE), “decision.values”)[,1]   ca_arr_pred = attr(predict(dataset_mod_short_ca19_9, newdata = dataset_train_data[,c(sig, “ca19_9”)], decision.values = TRUE), “decision.values”)[,1]   arr_pred = attr(predict(dataset_mod_short, newdata = dataset_test_data[,sig], decision.values = TRUE), “decision.values”)[,1]   ca_arr_pred = attr(predict(dataset_mod_short_ca19_9, newdata = dataset_test_data[,c(sig, “ca19_9”)], decision.values = TRUE), “decision.values”)[,1]   # tr_pred = arr_t_pred   # te_pred = arr_pred   # tr_y = as.factor(dataset_train_data$dx)   # te_y = as.factor(dataset_test_data$dx)     dv = arr_pred   ca19_9_dv = ca_arr_pred   y = dataset_test_data$dx   sig_roc_res_list[[i]] = tibble(    sample_name = dataset_test_data$sample_name,    dv,    ca19_9_dv,    ca19_9 = dataset_test_data$ca19_9,    dv_rank = rank(dv),    ca19_9_dv_rank = rank(ca19_9_dv),    y,    primary _diagnosis = dataset_test_data$primary_diagnosis)  }  sigs_roc_res = do.call(rbind,sig_roc_res_list)  sig_rank_res = sigs_roc_res %>% group_by(sample_name,y,primary_diagnosis) %>%   nest( ) %>%   mutate(ca19_9_dv_mean = map_dbl(data,function(x) mean(x$ca19_9_dv))) %>%   mutate(dv_mean = map_dbl(data,function(x) mean(x$dv)))  # subgroup analysis  sub_roc_data = left_join(sig_rank_res, dataset %>% select(sample_name, Original_Kit_ID, primary_diagnosis, sub_group,symp_diabetes, gender, age, site, collection_date, ca19_9))  sub_roc_data$stage_grp = “None”  sub_roc_data$stage_grp[sub_roc_data$sub_group %in% c(“Stage IA”,“Stage IB”,“Stage IIA”,“Stage IIB”) ] = “Early”  sub_roc_data$stage_grp[sub_roc_data$sub_group %in% c(“Stage III”,“Stage IV”) ] = “Late”  # All  par(mfrow=c(1,3))  all_sig_roc = pROC::roc(sig_rank_res$y,sig_rank_res$dv_mean, plot = TRUE, print.auc = TRUE, main = paste0(my_main,“: ”,“Array data, All”))  points(.95,.8, pch = 20, col = “red”)  lines(x = c(1,.95,.95), y = c(.8,.8,1), col = “red”,lty = “dotted”)  all_sig_roc_ca = pROC::roc(sig_rank_res$y,sig_rank_res$ca19_9_dv_mean, plot = TRUE, print.auc = TRUE, main = paste0(my_main,“: ”,“Array + CA19-9, All”))  points(.95,.8, pch = 20, col = “red”)  lines(x = c(1,.95,.95), y = c(.8,.8,1), col = “red”,lty = “dotted”)  all_sig_roc_caonly = pROC::roc(dataset$dx,dataset$ca19_9, plot = TRUE, print.auc = TRUE, main = paste0(my_main,“: ”,“CA19-9, All”))  points(.95,.8, pch = 20, col = “red”)  lines(x = c(1,.95,.95), y = c(.8,.8,1), col = “red”,lty = “dotted”)  # PDAC v Healthy  PDAC_v_healthy = subset(sub_roc_data, y == “PDAC” | primary_diagnosis ==“Healthy_Control”)  dataset$stage_grp = “None”  dataset$stage_grp[dataset$sub_group %in% c(“Stage IA”,“Stage IB”,“Stage IIA”,“Stage IIB”) ] = “Early”  dataset$stage_grp[dataset$sub_group %in% c(“Stage III”,“Stage IV”) ] = “Late”  PDAC_v_healthy_ca = subset(dataset, dx == “PDAC” | primary_diagnosis ==“Healthy_Control”) %>% select(ca19_9,dx)  if(length(unique(PDAC_v_healthy$y ))>1){   pvh_sig_roc = pROC::roc(PDAC_v_healthy$y,PDAC_v_healthy$dv_mean, plot = TRUE, print.auc = TRUE, main = paste0(my_main,“: ”,“Array data, PDAC vs. Healthy”))   points(.95,.8, pch = 20, col = “red”)   lines(x = c(1,.95,.95), y = c(.8,.8,1), col = “red”,lty = “dotted”)   pvh_sig_roc_ca = pROC::roc(PDAC_v_healthy$y,PDAC_v_healthy$ca19_9_dv_mean, plot = TRUE, print.auc = TRUE, main = paste0(my_main,“: ”,“Array + CA19-9, PDAC vs. Healthy”))   points(.95,.8, pch = 20, col = “red”)   lines(x = c(1,.95,.95), y = c(.8,.8,1), col = “red”,lty = “dotted”)   pvh_sig_roc_caonly = pROC::roc(PDAC_v_healthy_ca$dx,PDAC_v_healthy_ca$ca19_9, plot = TRUE, print.auc = TRUE, main = paste0(my_main,“: ”,“CA19-9, PDAC vs. Healthy”))   points(.95,.8, pch = 20, col = “red”)   lines(x = c(1,.95,.95), y = c(.8,.8,1), col = “red”,lty = “dotted”)  }  # PDAC v Symptomatic  PDAC_v_symptomatic = subset(sub_roc_data, y == “PDAC” | primary_diagnosis ==“No_PDAC_Control”)  PDAC_v_symptomatic_ca = subset(dataset, dx == “PDAC” | primary_diagnosis ==“No_PDAC_Control”) %>% select(ca19_9,dx)  if(length(unique(PDAC_v_symptomatic$y ))>1){   pvs_sig_roc = pROC::roc(PDAC_v_symptomatic$y,PDAC_v_symptomatic$dv_mean, plot = TRUE, print.auc = TRUE, main = paste0(my_main,“: ”,“Array data, PDAC vs. Symptomatic”))   points(.95,.8, pch = 20, col = “red”)   lines(x = c(1,.95,.95), y = c(.8,.8,1), col = “red”,lty = “dotted”)   pvs_sig_roc_ca = pROC::roc(PDAC_v_symptomatic$y,PDAC_v_symptomatic$ca19_9_dv_mean, plot = TRUE, print.auc = TRUE, main = paste0(my_main,“: ”,“Array + CA19-9, PDAC vs. Symptomatic”))   points(.95,.8, pch = 20, col = “red”)   lines(x = c(1,.95,.95), y = c(.8,.8,1), col = “red”,lty = “dotted”)   pvs_sig_roc_caonly = pROC::roc(PDAC_v_symptomatic_ca$dx,PDAC_v_symptomatic_ca$ca19_9, plot = TRUE, print.auc = TRUE, main = paste0(my_main,“: ”,“CA19-9, PDAC vs. Symptomatic”))   points(.95,.8, pch = 20, col = “red”)   lines(x = c(1,.95,.95), y = c(.8,.8,1), col = “red”,lty = “dotted”)  }  # PDAC v Early stage  Early_PDAC_v_Ctrl = subset(sub_roc_data, y == “Control” | stage_grp == “Early”)  Early_PDAC_v_Ctrl_ca = subset(dataset, dx == “Control” | stage_grp == “Early”) %>% select(ca19_9,dx)  if(length(unique(Early_PDAC_v_Ctrl$y ))>1){   pve_sig_roc = pROC::roc(Early_PDAC_v_Ctrl$y, Early_PDAC_v_Ctrl$dv_mean, plot = TRUE, print.auc = TRUE, main = paste0(my_main,“: ”,“Array data, Early PDAC vs. Ctrls”))   points(.95,.8, pch = 20, col = “red”)   lines(x = c(1,.95,.95), y = c(.8,.8,1), col = “red”,lty = “dotted”)   pve_sig_roc_ca = pROC::roc(Early_PDAC_v_Ctrl$y,Early_PDAC_v_Ctrl$ca19_9_dv_mean, plot = TRUE, print.auc = TRUE, main = paste0(my_main,“: ”,“Array + CA19-9, Early PDAC vs. Ctrls”))   points(.95,.8, pch = 20, col = “red”)   lines(x = c(1,.95,.95), y = c(.8,.8,1), col = “red”,lty = “dotted”)   pve_sig_roc_caonly = pROC::roc(Early_PDAC_v_Ctrl_ca$dx,Early_PDAC_v_Ctrl_ca$ca19_9, plot = TRUE, print.auc = TRUE, main = paste0(my_main,“: ”,“CA19-9, Early PDAC vs. Ctrls”))   points(.95,.8, pch = 20, col = “red”)   lines(x = c(1,.95,.95), y = c(.8,.8,1), col = “red”,lty = “dotted”)  }  AUC_RES = tibble(   Type = “ROC_AUC”,   all = all_sig_roc$auc[1],   all_ca = all_sig_roc_ca$auc[1],   all_ca_only = all_sig_roc_caonly$auc[1],   pvh = pvh_sig_roc$auc[1],   pvh_ca = pvh_sig_roc_ca$auc[1],   pvh_ca_only = pvh_sig_roc_caonly$auc[1],   pvs = pvs_sig_roc$auc[1],   pvs_ca = pvs_sig_roc_caSauc[1],   pvs_ca_only = pvs_sig_roc_caonly$auc[1],   pve = pve_sig_roc$auc[1],   pve_ca = pve_sig_roc_ca$auc[1],   pve_ca_only = pve_sig_roc_caonly$auc[1]  )  SEN_RES = tibble(   Type = “Sensitivity”,   all = thresh_to_sen(all_sig_roc,threshold_sig),   all_ca = thresh_to_sen(all_sig_roc_ca,threshold_sig_CA19),   all_ca_only = thresh_to_sen(all_sig_roc_caonly,threshold_CA19),   pvh = thresh_to_sen(pvh_sig_roc,threshold_sig),   pvh_ca = thresh_to_sen(pvh_sig_roc_ca,threshold_sig_CA19),   pvh_ca_only = thresh_to_sen(pvh_sig_roc_caonly,threshold_CA19),   pvs = thresh_to_sen(pvs_sig_roc,threshold_sig),   pvs_ca = thresh_to_sen(pvs_sig_roc_ca,threshold_sig_CA19),   pvs_ca_only = thresh_to_sen(pvs_sig_roc_caonly,threshold_CA19),     pve = thresh_to_sen(pve_sig_roc,threshold_sig),   pve_ca = thresh_to_sen(pve_sig_roc_ca,threshold_sig_CA19),   pve_ca_only = thresh_to_sen(pve_sig_roc_caonly,threshold_CA19)  )  SPE_RES = tibble(   Type = “Specificity”,   all = thresh_to_spe(all_sig_roc,threshold_sig),   all_ca = thresh_to_spe(all_sig_roc_ca,threshold_sig_CA19),   all_ca_only = thresh_to_spe(all_sig_roc_caonly,threshold_CA19),   pvh = thresh_to_spe(pvh_sig_roc,threshold_sig),   pvh_ca = thresh_to_spe(pvh_sig_roc_ca,threshold_sig_CA19),   pvh_ca_only = thresh_to_spe(pvh_sig_roc_caonly,threshold_CA19),   pvs = thresh_to_spe(pvs_sig_roc,threshold_sig),   pvs_ca = thresh_to_spe(pvs_sig_roc_ca,threshold_sig_CA19),   pvs_ca_only = thresh_to_spe(pvs_sig_roc_caonly,threshold_CA19),   pve = thresh_to_spe(pve_sig_roc,threshold_sig),   pve_ca = thresh_to_spe(pve_sig_roc_ca,threshold_sig_CA19),   pve_ca_only = thresh_to_spe(pve_sig_roc_caonly,threshold_CA19)  )  SEN_SEP_RES = rbind(AUC_RES,SPE_RES,SEN_RES)  return(SEN_SEP_RES) }

EXAMPLE 2

Objective: To validate the clinical predictive performance of the 8-plex antibody microarray-based biomarker panel in combination with CA19-9 intended for early detection of pancreatic ductal adenocarcinoma (PDAC).

Study design: The validation was performed as a blinded case-control study comprising 591 serum samples prospectively collected at 11 sites in USA and Europe. Samples were derived from 167 PDAC patients, 203 individuals at risk of hereditary or familial pancreatic cancer (FPC) and 221 healthy individuals, respectively.

Main outcome measures: Sensitivity, specificity, and positive and negative predictive values of the locked test model for detection of PDAC.

Results: The panel of eight serum biomarkers, in combination with CA19-9 assessment, effectively separated early-stage PDAC (stage I/II) patients from individuals belonging to the hereditary/FPC risk group with a specificity of 98% and a sensitivity of 85%. A specificity of 98% and a sensitivity of 87% were achieved when samples of all PDAC stages (stage I-IV) were included in the classification.

Conclusions: Using a blinded experimental protocol on a prospective multicenter collection of serum samples, we show that the validated clinical performance of our newly proposed biomarker panel is consistent with the results seen along the development pathway. The established classifier meets the requirements of a clinically useful test for identifying an individual at risk of having pancreatic cancer already in the early stage of disease.

Introduction

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a high rate of mortality. PDAC is the fourth most common cause of cancer death in the US with an estimate of 60,000 incident cases and 48,000 deaths in 2021 (Siegel et al, 2021). Less than 10% of patients survive 5 years after diagnosis (Ilic and Ilic, 2016). The main reason for this poor outcome is that only 10-20% of patients present with resectable disease at diagnosis, while the majority (50-60%) of patients have metastatic disease (Scheufele et al, 2019).

It is crucial to identify individuals at high risk of developing PDAC so preventive and early detection measures can be employed. The 5-year survival has been reported to be as great as 40% if the disease can be detected when it is still localized to the pancreas, mainly due to a higher number of surgically resectable tumors (Matsuno et al, 2004).

While most pancreatic cancers arise sporadically, a subset of neoplasms develop in patients with hereditary and familial predisposition. The familial or hereditary form of the disease accounts for 10 to 15% of cases (Copur et al, 2020). Several germline mutations have been shown to be associated to an increased risk of PDAC. For example, germline mutations involving BRCA1, BRCA2, CDKN2A and mismatch repair genes predispose patients to PDAC (Petersen, 2016). Familial pancreatic cancer (FPC) is defined as having first-degree relatives that had the disease, without an association to a known causative germline mutation or hereditary genetic syndrome. FPC accounts for 4 to 10% of PDAC cases (Copur et al, 2020).

This Example describes a completely independent blinded validation of the aggregate 9-biomarker signature (including CA19-9) from the first Example using a prospectively collected set of PDAC and control samples. To demonstrate clinical usefulness of the test, a large proportion of the control samples have been derived from subjects that belong to the hereditary and FPC risk group. This study relates to the ongoing clinical trial designated PanFAM-1 (clinicaltrials.gov).

Methods Sample Collection

All serum samples were prospectively collected through our study collaborators between 2019 and 2021 at eleven pancreatic disease reference sites in USA and Europe. The US sites contributing with samples in this study were Mount Sinai School of Medicine, Beth Israel Deaconess Medical Center, University of Pittsburgh Medical Center, BioIVT LLC., Discovery Life Sciences Inc., Massachusetts General Hospital and University of Pennsylvania. The sites in Europe contributing with samples were Sahlgrenska University Hospital and Vaxjo Central Hospital in Sweden, Helsinki University Hospital in Finland, and Ramon y Cajal University Hospital in Spain.

Demographic Information

The samples comprised 167 PDAC (whereof 56 stage I and II), 203 hereditary/FPC risk group controls and 221 healthy controls, respectively. The median age of donors was 70 for PDAC samples, 59 for hereditary/FPC risk group controls and 49 for healthy controls. The gender distribution for donors of PDAC samples were 58% male/42% female, 36% male/64% female for donors from the hereditary/FPC risk group, and 52% male/48% female for healthy controls.

Sample Randomization and Blinding

Serum samples were blinded to laboratory technicians, the laboratory director and the medical director until results were finalized. In addition, the order of samples was randomized within batches for biotinylation and microarray analysis to minimize analytical bias.

CA19-9 Assay

Serum CA19-9 levels were measured by a cobas e411 analyzer (Roche Diagnostics, Mannheim, Germany) using the electrochemiluminescence immunoassay technology. All CA19-9 assays were performed according to the manufacturer's instructions using a validated instrument and a defined SOP.

Sample Biotinylation

The serum samples were labeled with biotin in duplicate. Briefly, the serum was diluted 1:9 in PBS to a total protein concentration of approximately 8 g/I and labeled with 1.1 mM EZ-Link NHS-PEG4-Biotin (Thermo Fisher Scientific). The biotinylation was allowed to proceed for two hours at 4° C. and then quenched with the addition of 0.5M Tris-HCl, pH 8.0. The labeled serum samples were aliquoted and stored at −20° C. until further analysis.

Antibody Microarray Production

The antibody microarrays consisted of eight recombinant scFvs directed against various tumour antigens and complement system components. His-tagged scFvs were produced in the periplasm of E. coli and purified by immobilized metal ion affinity chromatography (IMAC) using His MultiTrap HP 96-well filter plates (GE Healthcare Life Science). The elution buffer was exchanged for PBS, using Zeba™ Spin desalting 96-well spin plates with 7K MWCO resins (Thermo Fisher Scientific). Protein concentration was estimated using a Pierce Coomassie (Bradford) protein assay kit (Thermo Fisher Scientific). Protein purity was assessed by SDS-PAGE using 8-16% Criterion TGX Stain-Free gels (Bio-Rad). Antibody microarrays were produced on black MaxiSorp slides (Nunc, Thermo Fisher Scientific), using a non-contact printer (sciFLEXARRAYER SX; Scienion). The array layout was printed using spot-on-the-fly option and with one PDC (glass piezo dispense capillary). Twelve identical arrays were printed on each slide in two columns of seven arrays.

Each array consisted of 6×10 spots with 350 μm spot-to-spot center distance and a spot diameter of 140 μm. Each array consisted of three identical segments. All scFvs were printed in six spot replicates with two replicates in each segment. The on-chip scFv concentrations ranged from 52 to 217 μg/mL depending on binding properties of the individual clones. In addition, on each array, six replicates of a negative control (PBS) and six replicates of a reference marker (biotinylated BSA) were spotted. Printed microarray slides were stored in dark in a climate-controlled room, at 22° C. and 40% relative humidity, for at least 5 days before being used in microarray assays.

Microarray Assay

In general, six patient samples were analysed in duplicate on each microarray slide. On each assay run, two microarray slides were dedicated for quality control (QC) and normalization purposes.

Each microarray slide was mounted in a hybridization gasket (Schott) and blocked with 1% w/v milk, 1% v/v Tween-20 in sterile PBS (MT-PBS) at room temperature (RT) for 2 hours with constant agitation. Meanwhile, aliquots of labelled serum samples were thawed on ice and subsequently diluted 1:50 in MT-PBS. The slides were washed four times with 0.05% Tween-20 in sterile PBS (T-PBS) followed by addition of diluted serum samples to the wells of the gasket.

Samples were incubated on the slides at RT for 2 hours with constant agitation. Next, the slides were washed four times with T-PBS, incubated with 1 μg/ml Streptavidin Alexa-647 (Life Technologies) in MT-PBS at RT for 1 hour with constant agitation, and again washed four times with T-PBS. Finally, the slides were dismounted from the gaskets, immersed in water and dried under a stream of N2. The slides were immediately scanned with a microarray fluorescence scanner, InnoScan 710 AL (Innopsys), at a laser excitation wavelength of 635 nm using a resolution of 10 μm/pixel.

Data Acquisition, Normalization and Quality Control

Data acquisition, normalization and quality control was generated automatically by our in-house, proprietary software platform, Immunovia Evaluation System LDT (IES LDT). In more detail, grid alignment and spot signal quantitation were performed using the fixed circle method.

Each data point represented the median, background-subtracted, signal of six replicate spots unless any replicate(s) did not pass the applied z-filtering acceptance criteria and was flagged as a position failure. In this case, the worst performing replicate(s) was eliminated and the median value of the remaining replicates was used instead. Log2 values of signal intensities were used, and no imputation was applied on the data.

Batch to batch variation was handled by automated calculation of normalization factors for each microarray assay batch, per each scFv antibody. These factors were based on the signals for six serum pools (three from healthy individuals and three from PDAC patients, respectively) placed on the dedicated QC slides.

Several quality control (QC) steps were automatically applied both before and after normalization and only samples passing the QC steps were further evaluated. In total, a single sample was removed and the final data set contained data from 591 individuals.

CA19-9 assay data was added into the dataset, and sample results in terms of a decision value (DV) for each patient sample were automatically calculated based on the 8-plex biomarker signature and the CA19-9 value. The results were automatically uploaded to Orchard Harvest Laboratory Information System (Orchard Software Corporation) for un-blinding and final result verification.

Data Analysis

Sensitivity, specificity, and positive and negative predictive values (PPV and NPV respectively) were calculated using predefined and locked model algorithm and a DV cut-off for sample classification. Receiver operating characteristic (ROC) curves, and the corresponding area under the curve (AUC) values, were calculated for the different diagnostic groups using the previously established and locked model algorithm.

Results

A total of 58 samples fell in the defined DV borderline range of the model algorithm and could not be assigned a classification. All other samples were successfully assayed and classified. This left 144 PDAC (whereof 46 stage I and II), 183 familial/hereditary controls and 206 healthy controls for analysis after completion of the study and unblinding of the sample information.

The results of the validation study were assessed by generating ROC curves for various group comparisons and the corresponding AUC values were calculated when combining data from the 8-plex microarray analysis with that from the CA19-9 assay. FIGS. 7-8 show an AUC value of 0.94 for classification of PDAC all stages (I-IV), and an AUC value of 0.92 for PDAC early stages (I/II), vs. controls belonging to the hereditary/FPC risk group, respectively. Applying the pre-defined model cut-off, a sensitivity of 87% and specificity of 98% are achieved for the comparison including all stages of PDAC, and most importantly, data yields a sensitivity of 85% and specificity of 98% for differentiating early PDAC patients from the individuals at risk of hereditary or FPC (but currently free of malignant disease) (Table 8).

TABLE 8 Results of the blind validation study PDAC vs familial/ Stage I & All stages hereditary controls II PDAC PDAC Sensitivity 84.8% 86.8% Specificity 98.4% 98.4% NPV (3% prevalence) 99.5% 99.6% PPV (3% prevalence) 62.1% 62.7% NPV (1% prevalence) 99.8% 99.9% PPV (1% prevalence) 34.9% 35.4%

FIGS. 9-10 show the corresponding ROC curves and AUC values for differentiating the PDAC samples from healthy controls (HC). The AUC values of 0.95 (PDAC stage I-IV vs. HC) and 0.93 (PDAC stage I/II vs. HC) are in good concordance with what has been observed during the development of the test (Mellby et al, 2018).

Moreover, we have assessed the positive and negative predictive values (PPV and NPV) of the test (Table 8). The PPV and NPV are related to the sensitivity and specificity through the prevalence of the disease (Trevethan, 2017). Depending on the particular genetic syndrome or the number of first-degree relatives diagnosed with PDAC, the prevalence can vary from 1-3% for the population at risk for hereditary/FPC. In Table 8, we show the result of calculations of PPV and NPV for the test at a prevalence of 1% and 3%, respectively. At 3% prevalence, the PPV is 62% for detection of early stage (I/II) PDAC. Since PPV is directly proportional to the prevalence, the value is significantly lower at 1%. The NPV for the detection of early stage (I/II) PDAC is high (99.5 and 99.8%) at both levels of prevalence.

FIG. 11 shows the distribution of decision values for the samples from the PDAC patients, the individuals at risk of hereditary or familial pancreatic cancer and the healthy donors, respectively. The DV is the output score of the test model that in conjunction with the cut-off value will inform whether a patient sample is positive or negative for PDAC. Interestingly, there is a clear distinction between PDAC and the other two groups, but a significant overlap between the individuals at risk of hereditary or familial pancreatic cancer and the healthy donors, although the aggregate health status is worse for the individuals in the risk group. There is a wide distribution of decision values for the patients with PDAC that could reflect the presence of different stages and the genetically heterogeneous nature of the disease (Juiz et al, 2019).

Discussion

In recent years, there has been an increased awareness of the benefits of germline genetic testing and family history analysis of PDAC patients to help to identify high-risk individuals (broadly defined as those with a lifetime risk greater than 5%). The general average lifetime risk of developing pancreatic cancer is too low (approximately 1.6% in the USA) for population-based screening. As a comparison, the estimated lifetime risk of developing pancreatic cancer for an individual with two first-degree relatives affected by the disease is around 8% (Klein et al, 2004). Identification of high-risk individuals provides an opportunity to detect PDAC at a potentially curative stage.

However, there is currently no general agreement on which groups of patients and individuals that should be eligible for pancreatic surveillance and screening. In 2020, the Cancer of the Pancreas Screening (CAPS) consortium released an update of their consensus guidelines for pancreatic cancer screening based on the clinical experiences of 49 multidisciplinary experts (Goggins et al, 2020). The consortium recommends screening in the following high-risk groups: Individuals who have at least one first-degree relative with pancreatic cancer who in turn also has a first-degree relative with pancreatic cancer, all patients with Peutz-Jeghers syndrome, all carriers of a germline CDKN2A mutation, and carriers of a germline BRCA1, BRCA2, PALB2, ATM, MLH1, MSH2, or MSH6 gene mutation with at least one affected first-degree blood relative.

Surveillance of individuals at high-risk of PDAC would typically include endoscopic ultrasound, computerised tomography and/or magnetic resonance imaging. Our validated test may serve in conjunction with these imaging methods to confirm or establish diagnosis. A test that can both increase the number of cancer cases referred to a specialist and increase the prevalence of cancers within that referred population, by minimizing the number of benign referrals, ought to be very useful.

To sum up, data from this blinded validation study demonstrates that our 8-plex antibody microarray assay, in combination with CA19-9 measurement, detects PDAC at an early stage (stage I&II) with a specificity of 98% and sensitivity of 85%. These results hold the potential of significantly expanding therapeutic possibilities and improving outcomes for patients affected by this devastating disease.

ADDITIONAL REFERENCES FOR EXAMPLE 2

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1. A method for diagnosing or determining a pancreatic cancer-associated disease state comprising or consisting of the steps of: (a) providing a sample from an individual to be tested; and (b) measuring the presence and/or amount in the test sample of two or more biomarkers selected from the group defined in Table A; wherein the presence and/or amount in the test sample of the two or more biomarkers selected from the group defined in Table A is indicative of the pancreatic cancer-associated disease state in the individual.
 2. The method according to claim 1 wherein the pancreatic cancer-associated disease state is selected from the group consisting of: (i) diagnosis of pancreatic cancer; (ii) diagnosis of early pancreatic cancer
 3. The method according to any one of the preceding claims wherein the pancreatic cancer-associated disease state is early pancreatic cancer.
 4. The method according to claim 3 wherein the method is for the diagnosis of stage I or stage II pancreatic cancer.
 5. The method according to any previous claim wherein the method is for distinguishing an individual with a pancreatic cancer-associated disease state from an individual without pancreatic cancer but with symptoms suggestive or consistent with pancreatic cancer, optionally wherein the individual without pancreatic cancer has a benign pancreatic or biliary disease, e.g. acute and chronic pancreatitis, diabetes, liver disease, pancreatic cyst, gallstone disease and IgG4 disease.
 6. The method according to any previous claim wherein the sample in step (a) is from an individual in one or more of the following risk groups: (a) Individuals with a family history of pancreatic cancer or certain hereditary predispositions (e.g. Peutz-Jeghers syndrome); (b) Individuals diagnosed with diabetes (e.g. new-onset diabetes, in particular those aged 50 years or over); and/or (c) Individuals with symptoms suggestive or consistent with pancreatic cancer.
 7. The method according to any previous claim wherein the sample in step (a) is from an individual with a benign pancreatic or biliary disease, e.g. acute and chronic pancreatitis, diabetes, liver disease, pancreatic cyst, gallstone disease and IgG4 disease.
 8. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of two or more biomarkers listed in Table A(i)-(vi), for example at least 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 of the biomarkers listed in Table A(i)-(vi).
 9. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of six or more biomarkers listed in Table A, for example at least 7, 8, 9, 10, 11, 12 or 13 of the biomarkers listed in Table A.
 10. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of three or more biomarkers listed in Table A(i)-(v), for example at least 4, 5, 6, 7, 8, or 9 of the biomarkers listed in Table A(i)-(v).
 11. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of one or more biomarker(s) listed in Table A, part (i) and/or part (iii) and/or part (v).
 12. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of: (i) OPG and/or VWF; and (ii) GSN and/or HADH2.
 13. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of: (i) OPG and/or VWF; (ii) GSN and/or HADH2; and (iii) IGFBP3.
 14. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of: (i) OPG and/or VWF; (ii) IGFBP3; and (iii) MUC16 and/or FCN2 and/or MASP2.
 15. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of: (i) OPG and/or VWF; (ii) GSN and/or HADH2; (iii) IGFBP3, and (iv) Complement Factor B.
 16. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of VWF, FCN2, and/or MASP2
 17. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of two or more of the following biomarkers: (i) OPG and/or VWF; (ii) GSN and/or HADH2; (iii) IGFBP3; (iv) MUC16, FCN2, and/or MASP2.
 18. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of: (i) OPG and/or VWF; (ii) GSN and/or HADH2; (iii) IGFBP3; (iv) Complement Factor B; (v) MUC16 and/or FCN2 and/or MASP2; (vi) Complement C4; (vii) Complement C5; and (viii) Cystatin C.
 19. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of two or more of the following biomarkers and/or one or more secondary target(s) thereof: OPG, GSN, IGFBP3, Complement Factor B, MUC16, Complement C4, Complement C5, Cystatin C; optionally wherein the one or more secondary targets are selected from VWF, HADH2, FCN2, MASP2.
 20. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of the following biomarkers: OPG, GSN, IGFBP3, Complement Factor B, MUC16, Complement C4, Complement C5, Cystatin C; and optionally including measuring the presence and/or amount of one or more biomarkers selected from VWF, HADH2, FCN2, MASP2.
 21. The method according to any one of the preceding claims wherein step (b) comprises measuring the presence and/or amount of CA 19-9.
 22. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of: (i) OPG and/or VWF; (ii) GSN and/or HADH2; and (iii) CA 19-9.
 23. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of: (i) OPG and/or VWF; (ii) GSN and/or HADH2; (iii) IGFBP3; and (iv) CA 19-9.
 24. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of: (i) OPG and/or VWF; (ii) IGFBP3; (iii) MUC16, FCN2 and/or MASP2; and (iv) CA 19-9.
 25. The method according to any one of the preceding claims wherein step (b) comprises or consists of measuring the presence and/or amount of: (i) OPG and/or VWF; (ii) GSN and/or HADH2; (iii) IGFBP3; (iv) Complement Factor B; (v) MUC16, FCN2 and/or MASP2; (vi) Complement C4; (vii) Complement C5; (viii) Cystatin C; and (ix) CA 19-9.
 26. The method according to any one of the preceding claims wherein step (b) comprises measuring the presence and/or amount of one or more biomarker(s) listed in Table 1, for example 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, or 33 of the biomarkers in Table
 1. 27. The method according to any one of the preceding claims wherein step (b) comprises measuring the presence and/or amount of all of the biomarkers listed in Table A (e.g. at the protein, mRNA and/or ctDNA level).
 28. The method according to any one of the preceding claims further comprising or consisting of the steps of: (c) providing one or more control samples from: i. an individual not afflicted with pancreatic cancer; and/or ii. an individual afflicted with a benign pancreatic and/or biliary disease; and (d) determining a biomarker signature of the one or more control samples by measuring the presence and/or amount in the control sample of the one or more biomarkers measured in step (b); wherein the pancreatic cancer-associated disease state is identified in the event that the presence and/or amount in the test sample of the one or more biomarkers measured in step (b) is different from the presence and/or amount in the control sample of the one or more biomarkers measured in step (d).
 29. The method according to any one of the preceding claims further comprising or consisting of the steps of: (e) providing one or more control samples from an individual afflicted with pancreatic cancer; and (f) determining a biomarker signature of the control sample by measuring the presence and/or amount in the control sample of the one or more biomarkers measured in step (b); wherein the pancreatic cancer-associated disease state is identified in the event that the presence and/or amount in the test sample of the one or more biomarkers measured in step (b) corresponds to the presence and/or amount in the control sample of the one or more biomarkers measured in step (f).
 30. The method according to claim 29 wherein the individual not afflicted with pancreatic cancer is a healthy individual.
 31. The method according to claim 29 or 30 wherein the one or more individual afflicted with pancreatic cancer is afflicted with a pancreatic cancer selected from the group consisting of adenocarcinoma (e.g., pancreatic ductal adenocarcinoma or tubular papillary pancreatic adenocarcinoma), pancreatic sarcoma, malignant serous cystadenoma, adenosquamous carcinoma, signet ring cell carcinoma, hepatoid carcinoma, colloid carcinoma, undifferentiated carcinoma, and undifferentiated carcinomas with osteoclast-like giant cells.
 32. The method according to any one of the preceding claims wherein the pancreatic cancer is pancreatic ductal adenocarcinoma.
 33. The method according to any one of the preceding claims wherein step (b) comprises measuring the expression of the protein or polypeptide of one or more biomarker(s).
 34. The method according to claim 33 wherein step (b), (d) and/or step (f) is performed using one or more first binding agent capable of binding to a biomarker protein or polypeptide listed in Table A.
 35. The method according to claim 34 wherein the one or more first binding agent comprises one or more of the antibody sequences defined in Table 5 and/or Table
 6. 36. The method according to claim 35 wherein the one or more first binding agent comprises one or more antibody sequences selected from the group consisting of: SEQ ID NO: 6, 11, 13, 15, 20, 30, 32, and
 36. 37. The method according to claim 35 wherein the one or more first binding agent comprises one or more of the antibody sequences SEQ ID NO: 30, 32, and
 36. 38. The method according to any one of claims 33-37 wherein the first binding agent comprises or consists of an antibody or an antigen-binding fragment thereof.
 39. The method according to claim 38 wherein the antibody or antigen-binding fragment thereof is a recombinant antibody or antigen-binding fragment thereof.
 40. The method according to any one of claims 35 to 38 wherein the antibody or antigen-binding fragment thereof is selected from the group consisting of: scFv; Fab; a binding domain of an immunoglobulin molecule.
 41. The method according to any one of claims 34 to 40 wherein the first binding agent is immobilised on a surface.
 42. The method according to one of the preceding claims wherein the one or more biomarkers in the test and/or control sample(s) are labelled with a detectable moiety.
 43. The method according to claim 42 wherein the detectable moiety is selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety; an enzymatic moiety.
 44. The method according to claim 42 or 43 wherein the detectable moiety is biotin.
 45. The method according to any one of claims 33 to 44 wherein step (b), (d) and/or step (f) is performed using an assay comprising a second binding agent capable of binding to the one or more biomarkers, the second binding agent comprising a detectable moiety.
 46. The method according to claim 45 wherein the second binding agent comprises or consists of an antibody or an antigen-binding fragment thereof.
 47. The method according to claim 46 wherein the antibody or antigen-binding fragment thereof is a recombinant antibody or antigen-binding fragment thereof.
 48. The method according to claim 46 or 47 wherein the antibody or antigen-binding fragment thereof is selected from the group consisting of: scFv; Fab; a binding domain of an immunoglobulin molecule.
 49. The method according to any one of claims 45 to 48 wherein the detectable moiety is selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety; an enzymatic moiety.
 50. The method according to claim 49 wherein the detectable moiety is fluorescent moiety (for example an Alexa Fluor dye, e.g. Alexa647).
 51. The method according to any one of the preceding claims wherein the method comprises or consists of an ELISA (Enzyme Linked Immunosorbent Assay).
 52. The method according to any one of the preceding claims wherein step (b), (d) and/or step (f) is performed using an array.
 53. The method according to claim 52 wherein the array is selected from the group consisting of: macroarray; microarray; nanoarray.
 54. The method according to any one of the preceding claims wherein the method comprises: (i) labelling biomarkers present in the sample with biotin; (ii) contacting the biotin-labelled proteins with an array comprising a plurality of scFv immobilised at discrete locations on its surface, the scFv having specificity for one or more of the proteins in Table A(i)-(vi); (iii) contacting the biotin-labelled proteins (immobilised on the scFv) with a streptavidin conjugate comprising a fluorescent dye; and (iv) detecting the presence of the dye at discrete locations on the array surface wherein the expression of the dye on the array surface is indicative of the expression of a biomarker from Table A in the sample.
 55. The method according to any one of claims 1 to 32 wherein step (b), (d) and/or (f) comprises measuring the expression of a nucleic acid molecule encoding the one or more biomarkers.
 56. The method according to claim 55, wherein measuring the expression of the one or more biomarker(s) in step (b), (d) and/or (f) is performed using one or more binding agent, each individually capable of binding selectively to a nucleic acid molecule encoding one of the biomarkers identified in Table A.
 57. The method according to any one of the preceding claims wherein the sample provided in step (a), (c) and/or (e) is selected from the group consisting of unfractionated blood, plasma, serum, tissue fluid, pancreatic tissue, milk, bile and urine.
 58. The method according to claim 57, wherein the sample provided in step (a), (c) and/or (e) is selected from the group consisting of unfractionated blood, plasma and serum.
 59. The method according to claim 57 or 58 wherein the sample provided in step (a), (c) and/or (e) is serum.
 60. The method according to any one of the preceding claims wherein the predictive accuracy of the method, as determined by an ROC AUC value, is at least 0.50, for example at least 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98 or at least 0.99.
 61. The method according to claim 60 wherein the predictive accuracy of the method, as determined by an ROC AUC value, is at least 0.83.
 62. The method according to any one of the preceding claims further comprising one or more further clinical investigations (such as testing a biopsy sample and/or in vivo imaging of the patient) in order to confirm or establish the diagnosis.
 63. The method according to any one of the preceding claims wherein, in the event that the individual is diagnosed with pancreatic cancer, the method comprises step (g) of providing the individual with a pancreatic cancer therapy.
 64. The method according to claim 63 wherein the pancreatic cancer therapy is selected from the group consisting of surgery, chemotherapy, radiotherapy, immunotherapy, chemoimmunotherapy, thermochemotherapy and combinations thereof.
 65. The method according to claim 63 or 64 wherein the pancreatic cancer therapy comprises or consists of surgical removal of the pancreas in whole or in part (for example, using the Whipple procedure to remove the pancreas head or a total pancreatectomy) combined with chemotherapy (for example, gemcitabine and/or 5-fluorouracil).
 66. The method according to any previous claim wherein measuring the presence and/or amount in the test sample of one or more biomarkers selected from the group defined in Table A in step (b) is replaced with measuring the presence and/or amount in the test sample of one or more protein bound by one or more of the antibody sequences described in Table
 5. 67. An array for determining the presence of, or risk of having, pancreatic cancer in an individual comprising an agent or agents for detecting the presence in a protein and/or nucleic acid sample from the individual of one or more of the biomarkers defined in Table A.
 68. The array according to claim 67 wherein the agent or agents for detecting the presence in a sample of one or more of the biomarkers defined in Table A is/are one or more binding agents as defined in any one of claim 34 to 44 or
 56. 69. The array according to claim 67 or 68 wherein the array comprises agents capable of binding to all of the biomarkers defined in Table A(i)-(vi); optionally wherein the array comprises agents capable of binding to all of the biomarkers defined in Table A.
 70. The array according to claim 67 or 68 wherein the array comprises agents capable of binding to the following biomarkers: (i) OPG and/or VWF; (ii) GSN and/or HADH2; (iii) IGFBP3; (iv) Complement Factor B; (v) MUC16, FCN2 and/or MASP2; (vi) Complement C4; (vii) Complement C5; and (viii) Cystatin C; optionally including one or more additional biomarkers from Table
 1. 71. The array according to any one of claims 67 to 70 wherein the array comprises antibodies, or antigen-binding fragments thereof, capable of binding to all of the biomarkers at the protein level.
 72. The array according to any one of claims 67 to 71 wherein the array comprises one or more of the antibodies identified in Table
 5. 73. The array according any one of claims 67 to 72 wherein the array comprises one or more of the antibodies in Table
 6. 74. Use of two or more biomarkers selected from the group defined in Table A as biomarkers for determining the presence of, or risk of having, pancreatic cancer in an individual.
 75. The use according to claim 74 wherein the two or more biomarkers comprise the following biomarkers: (i) OPG and/or VWF; (ii) GSN and/or HADH2; (iii) IGFBP3; (iv) Complement Factor B; (v) MUC16, FCN2 and/or MASP2; (vi) Complement C4; (vii) Complement C5; and (viii) Cystatin C; optionally the use additionally including use of CA 19-9 and/or one or more additional biomarkers from Table
 1. 76. The use according to claim 74 or 75 wherein all of the biomarkers defined in Table A are used together as a diagnostic signature for determining the presence of pancreatic cancer in an individual.
 77. A kit for determining the presence of, or risk of having, pancreatic cancer comprising: (a) an array according to any one of claims 67 to 73, or components for making the same; and (b) instructions for performing the method as defined in any one of claims 1 to
 66. 78. A method of treating pancreatic cancer in an individual comprising the steps of: (a) diagnosing pancreatic cancer according to the method defined in any one of claims 1 to 66; and (b) providing the individual with pancreatic cancer therapy.
 79. The method according to claim 78 wherein step (a) further comprises comprise one or more further clinical investigations (such as testing a biopsy sample and/or in vivo imaging of the patient) in order to confirm or establish the diagnosis.
 80. The method according to claim 78 or 79 wherein the pancreatic cancer therapy is selected from the group consisting of surgery (e.g., resection), chemotherapy, immunotherapy, chemoimmunotherapy and thermochemotherapy.
 81. The method of any one of claims 78 to 80 wherein the pancreatic cancer therapy comprises surgical removal of the pancreas in whole or in part (e.g. using the Whipple procedure to remove the pancreas head or a total pancreatectomy) combined with chemotherapy (e.g. gemcitabine and/or 5-fluorouracil).
 82. A method or use for determining the presence of pancreatic cancer in an individual substantially as described herein.
 83. An array or kit for determining the presence of pancreatic cancer in an individual substantially as described herein. 